# Working with Amazon Quick Sight Topics Working with Topics | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | *Topics* are collections of one or more datasets that represent a subject area that your business users can ask questions about. With Quick Sight automated data prep, you get an ML-powered assist to help you create a topic that is relevant to your end users. The first process begins with automated field selection and classification, something like this: + Automated data prep chooses a small number of fields to include by default to create a focused data space for readers to explore. + Automated data prep selects fields that you use in other assets like reports and dashboards. + Automated data prep also imports any additional fields from any related analysis where a topic is enabled. + It identifies dates, dimensions, and measures, to learn how fields can be used in answers. This automatic set of fields help the author quickly get started with natural language analytics. Authors can always exclude fields, or include additional fields, as needed by using the **Include** toggle. Next, automated data prep continues with the process by automatically labeling fields and identifying synonyms. Automated data prep updates field names with friendly names and synonyms using common terms. For example, a `SLS_PERSON` field might be renamed to `Sales person`, and assigned synonyms including: `salesman`, `saleswoman`, agent, and `sales representative`. Although you can let automated data prep do much of the work, it's worthwhile to review the fields, names, and synonyms to further customize them for your end users. For example, if the users refer to a sales person as a "rep" or a "dealer" in casual conversation, then you support this term by adding `rep` and `dealer` to the synonyms for `SLS_PERSON`. Finally, automated data prep detects the semantic type of each field, by sampling its data and examining the formats applied to it by the author during analysis. Automated data prep updates the field configuration automatically, setting formats for values used for each field. Answers to questions are thus provided in expected formats for dates, currencies, identifiers, Booleans, persons, and so on. To learn more about working with topics, continue on to the following sections in this chapter. **Topics** + [ # Navigating Topics ](navigating-topics.md) + [ # Creating Quick Sight topics ](topics-create.md) + [ # Topic workspace ](topics-interface.md) + [ # Working with datasets in an Quick Sight topic ](topics-data.md) + [ # Making Quick Sight topics natural-language-friendly ](topics-natural-language.md) + [ # Sharing Quick Sight topics ](topics-sharing.md) + [ # Managing Amazon Quick Sight topic permissions ](topics-sharing-permissions.md) + [ # Reviewing Quick Sight topic performance and feedback ](topics-performance.md) + [ # Refreshing Quick Sight topic indexes ](topics-index.md) + [ # Work with Quick Sight topics using the Amazon Quick Sight APIs ](topics-cli.md) # Navigating Topics In Quick Sight, there is more than one way to create and manage a topic. You can begin on an Amazon Quick home or "start" page. Or, you can begin inside of an analysis. **Topics** + [ # From an Amazon Quick home page ](starting-from-home.md) + [ # From an Amazon Quick Sight analysis ](starting-from-sheets.md) + [ # Navigating questions in an Amazon Quick Sight analysis ](starting-from-questions-on-sheets.md) # From an Amazon Quick home page From your Quick start page, you can create and manage topics by selecting **Topics** in the navigation pane at left. Quick provides a guided workflow for creating topics. You can step out of the guided workflow and come back to it later, without disrupting your work. When you create a topic, your business users can ask questions about it. At any time, you can open a topic to change it or review how it's performing. To open a topic, choose the topic name. If at any time you want to return to a list of all your topics, choose **All topics** at left of the topic workspace. # From an Amazon Quick Sight analysis To start from an Amazon Quick Sight analysis, open the analysis that you want to use with automated data prep . To open or create a topic, choose the topic icon in the top navigation bar. At any time, you can open a topic to change it or review how it's performing. To open a topic from an analysis, choose the topic name in the top navigation bar, if it isn't already displayed. Then select the vertical ellipsis icon (` ⋮ `) on the top navigation bar. To view information about the topic, select **About topic**. To view the data fields included in the topic, select **Data fields** in the tab list. # Navigating questions in an Amazon Quick Sight analysis Navigating questions and answers By navigating through the questions and answers for a topic in an analysis, you can learn how the topic is being used. This information can inform you to make adjustments if necessary. Starting from within an analysis that is already linked to a topic, select the search bar on the top navigation bar and then enter a question. The answer displays on a topic screen that also displays all the available options to work with the topic in an analysis. + To change the type of visual displayed in the answer, select the type icon (which resembles a bar chart). + To view improvement suggestions, select the speech bubble, which is highlighted if you have unviewed suggestions. + To view insights related to a question, select the light bulb icon. + To add or remove a question from the pinboard, toggle the icon for **Add to pinboard** or **Remove from pinboard**. You can view the pinboard by selecting the pinboard icon from the top navigation bar. + To view information about this topic, select the circled lowercase *i* (` ![\[alt text not found\]](http://docs.aws.amazon.com/quick/latest/userguide/images/status-info.png) `). + Select the ellipsis menu ( ` … `) to do one of the following actions: + **Export to CSV** – Export the data displayed in the selected visual. + **Copy Request ID** – Capture the request ID of this process for troubleshooting. Amazon Quick Sight generates an alphanumeric request ID to uniquely identify each process. + **Share this visual** – Securely share a URL for the topic used in the visual. + **Answer breakdown** – To view a detailed explanation of your answer. At the bottom of the topic screen, you can add or change variations on the question by selecting **Edit question variants**. Also at the bottom, when you are satisfied with the question and answer, mark the topic as reviewed by choosing **Mark as reviewed**. Or, if you see that a previously reviewed topic needs further review, choose **Unmark as reviewed**. At any time, you can open a topic to change it or review how it's performing. To work directly with the settings for a topic, such as which fields are included, or what synonyms they have, use the **Topics** page. **To open a topic linked to an analysis** 1. Open the Amazon Quick Sight **Topics** page from the Quick start page, by selecting **Topics** in the navigation pane at left. If you want to keep your analysis open, you can open the **Topics** page in a new browser tab or window. 1. To open a topic, choose the topic name. If you recently navigated away from the analysis page, the name is probably still displayed in the search bar at the top of the screen. 1. If at any time you want to return to a list of all your topics, choose **All topics** at left of the topic workspace. # Creating Quick Sight topics Creating topics | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | To turn on questions for your datasets, you have to create a topic. Quick Sight provides a guided workflow for creating topics. You can step out of the guided workflow and come back to it later, without disrupting your work. There are two ways to create a topic: + Create the topic by selecting a dataset. When you create topics in Quick Sight, you can add multiple datasets to them and also enable the topics in analyses. + Create the topic using an analysis. When you create a topic in an analysis, or link an existing topic to an analysis, automated data prep learns from how you analyze your data and automatically applies this to your topic. After you share your topic with Quick readers and they use it to ask questions in the search bar, you can see a summary of how the topic is performing. You can also see a list of everything users asked and how well it was responded to, and any answers you have verified. Reviewing the feedback is important so that your business users can continue to be provided with the correct visualizations and answers to their questions. ## Creating a topic Use the following procedure to create a topic. **To create a topic** 1. On the Quick homepage, choose **Topics**. 1. On the **Topics** page that opens, choose **Create Topic** at upper right. 1. On the **Create Topic** page that opens, do the following: 1. For **Topic name**, enter a descriptive name for the topic. Your business users identify the topic by this name and use it to ask questions. 1. For **Description**, enter a description for the topic. Your users can use this description to get more details about the topic. 1. Choose **Continue**. 1. On the **Add data to topic** page that opens, choose one of the following options: + To add one or more datasets that you own or have permission to, choose **Datasets**, and then select the dataset or datasets that you want to add. + To add datasets from dashboards that you have created or that have been shared with you, choose **Datasets from a dashboard**, and then select a dashboard from the list. 1. Choose **Add data**. Your topic is created and the page for that topic opens. The next step is to configure the topic metadata to make it natural-language-friendly for your readers. For more information, see [Making Quick Sight topics natural-language-friendly](topics-natural-language.md). Or continue to the next topic to explore the topic workspace. # Topic workspace | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | After you create a topic, or when you choose an existing topic from the list on the **Topics** page, the topic opens to that topic's workspace. Four tabs appear here that you can use as described in the following sections. Quick Sight provides a guided workflow for topics. You can step out of the guided workflow and come back to it later, without disrupting your work. ## Summary The **Summary** tab has three important areas: + **Suggestions** – Suggestions provide step-by-step guidance for how you can improve a topic. These steps help you understand how to create better-performing topics. To follow a suggestion, choose the action button in the Suggestion banner and follow the recommended steps. Currently, there are eight preset suggestions that is offered in the order shown by the following table. After you complete a step for a suggestion, a new suggestion is offered when you return to the **Summary** tab. [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/quick/latest/userguide/topics-interface.html) + **Metrics and key performance indicators (KPIs) on topic engagement and performance** – In this section, you can see how your readers engage with your topics and what feedback and ratings they give on the answers provided. You can view engagement for all the questions users asked, or select a specific question. You can also change the time span of the metrics from one year down to one week. For more information, see [Reviewing Quick Sight topic performance and feedback](topics-performance.md). + **Datasets** – This section shows the datasets that were used to create the topic. In this section, you can add additional datasets or import datasets from existing dashboards. You can also edit the metadata for a topic dataset, set a data refresh schedule, change the name of the dataset, and more. For more information, see [Working with datasets in an Quick Sight topic](topics-data.md). ## Data The **Data** tab shows all the fields included in the topic. Here you configure your topic metadata to make your topic natural-language-friendly and to improve your topic performance. For more information, see [Making Quick Sight topics natural-language-friendly](topics-natural-language.md). ## User activity This tab shows all the questions that your topic receives and the overall feedback for each question. You can see an overview of how many questions were asked and what percentage of them were positive and negative. You can filter by feedback and whether someone left a comment with their feedback. For more information, see [Reviewing Quick Sight topic performance and feedback](topics-performance.md). ## Verified answers *Verified answers* are questions that you have preconfigured visuals for. You can create a verified answer to a question by asking the question in the search bar and then marking it as reviewed. By using the **Verified Answers** tab, you can review your verified answers and the feedback they receive by your users. # Working with datasets in an Quick Sight topic Working with datasets in a topic | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | When you create a topic, you can add additional datasets to it or import datasets from existing dashboards. At any time, you can edit metadata for a dataset and set a data refresh schedule. You can also add new fields to a dataset in a topic by creating calculated fields, filters, or named entities. **Topics** + [ # Adding datasets to a topic in Amazon Quick Sight ](topics-data-add.md) + [ # Adding datasets with row-level security (RLS) to a Amazon Quick Sight topic ](topics-data-rls.md) + [ # Refreshing datasets in a Quick Sight topic ](topics-data-refresh.md) + [ # Removing datasets from a Amazon Quick Sight topic ](topics-data-remove.md) + [ # Adding calculated fields to a Amazon Quick Sight topic dataset ](topics-data-calculated-fields.md) + [ # Adding filters to a Amazon Quick Sight topic dataset ](topics-data-filters.md) + [ # Adding named entities to a Amazon Quick Sight topic dataset ](topics-data-entities.md) # Adding datasets to a topic in Amazon Quick Sight Adding datasets At any time, you can add datasets to a topic. Use the following procedure to learn how. **To add datasets to a topic** 1. Open the topic that you want to add one or more datasets to. 1. On the **Summary** page, choose **Data**. Then, under **Datasets**, choose **Add datasets**. 1. On the **Add datasets** page that opens, choose the dataset or datasets that you want to add, and then choose **Add datasets**. The dataset is added to the topic and the dataset's unique string values are indexed. You can edit the field configurations right away. For more information, see [Refreshing Quick Sight topic indexes](topics-index.md). For more information about editing field configurations , see [Making Quick Sight topics natural-language-friendly](topics-natural-language.md). # Adding datasets with row-level security (RLS) to a Amazon Quick Sight topic You can add datasets that contain row-level security (RLS) to topics. All fields in a topic respect the RLS rules applied to your dataset. For example, if a user asks, "show me sales by region," the data that is returned is based on the user's access to the underlying data. So, if they're only allowed to see the East region, only data for the East region appears in the answer. RLS rules are applied to automatic suggestions when users are asking questions. As users enter questions, only the values that they have access to are suggested to them. If a user enters a question about a dimensional value that they don't have access to, they do not get an answer for that value. For example, suppose that the same user is entering the question, "show me sales in the West region." In this case, they do not get a suggestion or an answer for it, even if they ask, because they don't have RLS access to that region. By default, Quick Sight allows users to ask questions regarding fields based on the user's permissions in RLS. Continue to use this option if your field contains sensitive data that you want to restrict access to. If your fields don't contain sensitive information and you want all users to see the information in suggestions, then you can choose to allow questions for all values in the field. **To allow questions for all fields** 1. From the Quick homepage, choose **Data**. 1. Under the **Datasets** tab, choose the dataset that you added RLS to, and then choose **Edit dataset**. For more information about adding RLS to a dataset, see [Using row-level security in Amazon Quick](row-level-security.md). 1. On the data preparation page, choose the field menu (the three dots) for a field that you want to allow , and then choose **Row level security **. 1. On the **Row level security for Quick** page that opens, choose **Allow users to ask questions regarding all values on this field**. 1. Choose **Apply**. 1. When finished editing the dataset, choose **Save & publish** in the blue toolbar at upper right. 1. Add the dataset to your topic. For more information, see the previous section, [Adding datasets to a topic in Amazon Quick Sight](topics-data-add.md). If you currently allow users to ask questions regarding all values, but want to implement the dataset's RLS rules to protect sensitive information, then repeat steps 1–4 and choose **Allow users to ask questions regarding this field based on their permissions**. When you are done, refresh the dataset in your topic. For more information, see [Refreshing datasets in a Quick Sight topic](topics-data-refresh.md). # Refreshing datasets in a Quick Sight topic Refreshing datasets When you add a dataset to a topic, you can specify how often you want that dataset to refresh. When you refresh datasets in a topic, the index is refreshed for that topic with any new and updated information. Your datasets aren't replicated when you add them to a topic. An index of unique string values is created and metrics are not indexed. For example, measures stored as integers are not indexed. Questions asked always fetch the latest sales metrics based on data in your dataset. For more information about refreshing the topic index, see [Refreshing Quick Sight topic indexes](topics-index.md) You can set a refresh schedule for a dataset in a topic, or refresh the dataset manually. You can also see when the data was last refreshed. **To set a refresh schedule for a topic dataset** 1. Open the topic that you want to change. 1. On the **Summary** page, choose **Data**. Then, under **Datasets**, expand the dataset that you want to set a refresh schedule for. 1. Choose **Add schedule**, and then do one of the following in the **Add refresh schedule** page that opens. + If the dataset is a SPICE dataset, select **Refresh topic when dataset is imported into SPICE**. Currently, hourly refresh SPICE datasets aren't supported. SPICE datasets that are set to refresh every hour are automatically converted to a daily refresh. For more information about setting refresh schedules for SPICE datasets, see [Refreshing SPICE data](refreshing-imported-data.md). + If the dataset is a direct query dataset, do the following: 1. For **Timezone**, choose a time zone. 1. For **Repeats**, choose how often you want the refresh to happen. You can choose to refresh the dataset daily, weekly, or monthly. 1. For **Refresh time**, enter the time that you want the refresh to start. 1. For **Start first refresh on**, choose a date that you want start refreshing the dataset on. 1. Choose **Save**. **To manually refresh a dataset** 1. On the topic **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset that you want to refresh. 1. Choose **Refresh now**. **To view refresh history for a dataset** 1. On the topic **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset that you want to see refresh history for. 1. Choose **View history**. The **Update history** page opens with a list of the times the dataset was refreshed. # Removing datasets from a Amazon Quick Sight topic Removing datasets You can remove datasets from a topic. Removing datasets from a topic doesn't delete them from Quick Sight. Use the following procedure to remove a dataset from a topic. **To remove a dataset from a topic** 1. Open the topic that you want to change. 1. On the **Summary** page, choose **Data**. Then, under **Datasets**, choose the dataset menu (the three dots) at right, and then choose **Remove from topic**. 1. On the **Are you sure you want to delete?** page that opens, choose **Delete** to remove the dataset from the topic. Choose **Cancel** if you don't want to remove the dataset from the topic. # Adding calculated fields to a Amazon Quick Sight topic dataset Adding calculated fields You can create new fields in a topic by creating calculated fields. *Calculated fields* are fields that use a combination of one or two fields from a dataset with a supported function to create new data. For example, if your dataset contains columns for sales and expenses, you can combine them in a calculated field with a simple function to create a profit column. The function might look like the following: `sum({Sales}) - sum({Expenses})`. **To add a calculated field to a topic** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. For **Actions**, choose **Add calculated field**. 1. In the calculations editor that opens, do the following: 1. Give the calculated field a friendly name. 1. For **Datasets** at right, choose a dataset that you want to use for the calculated field. 1. Enter a calculation in the calculation editor at left. You can see a list of fields in the dataset in the **Fields** pane at right. You can also see a list of supported functions in the **Functions** pane at right. For more information about the functions and operators you can use to create calculations in Quick Sight, see the [Calculated field function and operator reference for Amazon QuickFunctions and operators](calculated-field-reference.md). 1. When finished, choose **Save**. The calculated field is added to the list of fields in the topic. You can add a description to it and configure metadata for it to make it more natural language friendly. # Adding filters to a Amazon Quick Sight topic dataset Adding filters Sometimes your business users (readers) might ask questions that contain terms that map to multiple cells of values in the data. For example, let's say one of your readers asks, "Show me weekly sales trend in the west." *West* in this instance refers to both the `Northwest` and `Southwest` values in the `Region` field, and requires the data to be filtered to generate an answer. You can add filters to a topic to support requests like these. **To add a filter to a topic** 1. Open the topic that you want to add a filter to. 1. In the topic, choose the **Data** tab. 1. For **Actions**, choose **Add filter**. 1. In the **Filter configuration** page that opens, do the following: 1. For **Name**, enter a friendly name for the filter. 1. For **Dataset**, choose a dataset that you want to apply the filter to. 1. For **Field**, choose the field that you want to filter. Depending on the type of field you choose, you're offered different filtering options. + If you chose a text field (for example, `Region`), do the following: 1. For **Filter type**, choose the type of filter that you want. For more information about filter text fields, see [Adding text filters](add-a-text-filter-data-prep.md). 1. For **Rule**, choose a rule. 1. For **Value**, enter one or more values. + If you chose a date field (for example, `Date`), do the following: 1. For **Filter type**, choose the type of filter that you want, and then enter the date or dates that you want to apply the filter to. For more information about filtering dates, see [Adding date filters](add-a-date-filter2.md). + If you chose a numeric field (for example, `Compensation`), do the following: 1. For **Aggregation**, choose how you want to aggregate the filtered values. 1. For **Rule**, choose a rule for the filter, and then enter a value for that rule. For more information about filtering numeric fields, see [Adding numeric filters](add-a-numeric-filter-data-prep.md). 1. (Optional) To specify when the filter is applied, choose **Apply the filter anytime the dataset is used**, and then choose one of the following: + **Apply always** – When you choose this option, the filter is applied whenever any column from the dataset you specified is linked to a question. + **Apply always, unless a question results in an explicit filter from the dataset** – When you choose this option, the filter is applied whenever any column from the dataset you specified is linked to a question. However, if the question mentions an explicit filter on the same field, the filter isn't applied. 1. When finished, choose **Save**. The filter is added to the list of fields in the topic. You can edit the description for it or adjust when the filter is applied. # Adding named entities to a Amazon Quick Sight topic dataset Adding named entities When asking questions about your topic, your readers might refer to multiple columns of data without stating each column explicitly. For example, they might ask for the address of a transaction. What they actually mean is that they want the branch name, state, and city of where the transaction was made. To support requests like this, you can create a named entity. A *named entity* is a collection of fields that display together in an answer. For example, using the transaction address example, you can create a named entity called `Address`. You can then add the `Branch Name`, `State`, and `City` columns to it, which already exist in the dataset. When someone asks a question about address, the answer displays the branch, state, and city where a transaction took place. **To add a named entity to a topic** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. For **Actions**, choose **Add named entity**. 1. In the **Named entity** page that opens, do the following: 1. For **Dataset**, choose a dataset. 1. For **Name**, enter a friendly name for the named entity. 1. For **Description**, enter a description of the named entity. 1. (Optional) For **Synonyms**, add any alternate names that you think your readers might use to refer to the named entity or the data it contains. 1. Choose **Add field**, and then choose a field from the list. Choose **Add field** again to add another field. The ordering of the fields listed here are the order they appear in answers. To move a field, choose the six dots at left of the field name and drag and drop the field to the order that you want. 1. When finished, choose **Save**. The named entity is added to the list of fields in the topic. You can add edit the description for it and add synonyms to it to make it more natural language friendly. # Making Quick Sight topics natural-language-friendly Making topics natural-language-friendly | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | When you create a topic, Quick Sight creates, stores, and maintains an index with definitions for data in that topic. This index is used to generate correct answers, provide autocomplete suggestions when someone asks a question, and suggest mappings of terms to columns or data values. This is how key terms can be interpreted in your readers' questions and mapped to your data. To help interpret your data and better answer your readers' questions, provide as much information about your datasets and their associated fields as possible. Use the following procedures to do so, making your topics more natural-language-friendly. **Tip** You can edit multiple fields at a time using bulk actions. Use the following procedure to bulk-edit fields in a topic. **To bulk-edit fields in a topic** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. Under **Fields**, select two or more fields that you want to change. 1. Choose **Bulk Actions** at the top of the list. 1. In the **Bulk Actions** page that opens, configure the fields how you want, and then choose **Apply to**. The configuration options are described in the following steps. ## Step 1: Give datasets friendly names and descriptions Dataset names are often based on technical naming conventions that your readers might not naturally use to refer to them. We recommend that you give your datasets friendly names and descriptions to provide more information about the data they contain. These friendly names and descriptions are used to understand dataset contents and select a dataset based on the reader's question. The dataset names are also shown to the reader to provide additional context for an answer. For example, if your dataset is named `D_CUST_DLY_ORD_DTL`, you might rename it in the topic to `Customer Daily Order Details`. That way, when your readers see it listed in the search bar for your topic, they can quickly determine if the data is relevant to them or not. **To give a dataset a friendly name and description** 1. Open the topic that you want to change. 1. On the **Summary** tab, choose **Data**. Then, under **Datasets**, choose the down arrow at the far right of the dataset to expand it. 1. Choose the pencil icon next to the dataset name at left, and then enter a friendly name. We recommend using a name that your readers will understand. 1. For **Description**, enter a description for the dataset that describes the data it contains. ## Step 2: Instruct how to use date fields in your datasets If your dataset contains date and time information, we recommend instructing how to use that information when answering questions. Doing this is especially important if you have multiple date time columns in a topic. In some cases, there are multiple valid date columns in a topic, such as order date and shipped date. In these cases, you can help readers by specifying a default date to use to answer their questions. Readers can choose a different date if the default date doesn't answer their question. You can also tell how granular to be with your date time columns by specifying a time basis. The *time basis* for a dataset is the lowest level of time granularity that is supported by all measures in the dataset. This setting helps aggregate metrics in the dataset across different time dimensions and is applicable for datasets that support a single date time granularity. This option can be set for denormalized datasets with a large number of metrics. For example, if a dataset supports several metrics at a daily aggregation, then you can set the time basis of that dataset to **Daily**. This is then used to determine how to aggregate metrics. **To set a default date and time basis for a dataset** 1. Open the topic that you want to change. 1. On the **Summary** tab, choose **Data**. Then, under **Datasets**, choose the down arrow at far right of the dataset to expand it. 1. For **Default date**, choose a date field. 1. For **Time basis** choose the lowest level of granularity that you want to aggregate metrics in the dataset to. You can aggregate metrics in a topic at the daily, weekly, monthly, quarterly, or yearly level. ## Step 3: Exclude unused fields When you add a dataset to a topic, all columns (fields) in the dataset are added by default. If your dataset contains fields that you or your readers don't use, or that you don't want to include in answers, you can exclude them from the topic. Excluding these fields removes them from answers and the index and improves the accuracy of answers that your readers receive. **To exclude fields in a topic** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, under **Include**, toggle the icon off. ## Step 4: Rename fields to be natural-language-friendly Fields in a dataset are often named based on technical naming conventions. You can make your field names more user-friendly in your topics by renaming them and adding descriptions. Field names are used to understand the fields and link them to terms in your readers' questions. When your field names are user-friendly, it's easier to draw links between the data and a reader’s question. These friendly names are also presented to readers as part of the answer to their question to provide additional context. **To rename and add descriptions to a field** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right of the field to expand it. 1. Choose the pencil icon next to the field name at left, and then enter a friendly name. 1. For **Description**, enter a description of the field. ## Step 5: Add synonyms to fields and field values Even if you update your field names to be user-friendly and provide a description for them, your readers might still use different names to refer to them. For example, a `Sales` field might be referred to as `revenue`, `rev`, or `spending` in your reader's questions. To help make sense of these terms and map them to the correct fields, you can add one or more synonyms to your fields. Doing this improves accuracy. As with field names, your readers might use different names to refer to specific values in your fields. For example, if you have a field that contains the values `NW`, `SE`, `NE`, and `SW`, you can add synonyms for those values. You can add `Northwest` for `NW`, `Southeast` for `SE`, and so on. **To add synonyms for a field** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, under **Synonyms**, choose the pencil icon for the field, enter a word or phrase, and then press Enter on your keyboard. To add another synonym, choose the **\$1** icon. **To add synonyms for a value in a field** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right to expand information about the field. 1. Under **Value Preview** at right, choose **Configure value synonyms**. 1. On the **Field Value Synonyms** page that opens, choose **Add**, and then do the following: 1. For **Value**, choose the value that you want to add synonyms to. 1. For **Synonyms**, enter one or more synonyms for the value. 1. Choose **Save**. 1. To add synonyms for another value, repeat steps 5–6. 1. When you finish, choose **Done**. ## Step 6: Explain more about your fields To help interpret how to use your data to answer readers' questions, you can explain more about the fields in your datasets. You can say whether a field in your dataset is a dimension or a measure and specify how that field should be aggregated. You can also clarify how the values in a field should be formatted, and what type of data is in the field. Configuring these additional settings helps create accurate answers for your readers when they ask a question. Use the following procedures to explain more about your fields. ### Assign field roles Every field in your dataset is either a dimension or a measure. *Dimensions* are categorical data, and *measures* are quantitative data. Knowing whether a field is a dimension or a measure determines what operations can and can't perform on a field. For example, setting the fields `Patient ID`, `Employee ID`, and `Ratings` helps interpret those fields as integers. This setting means that the fields will not be aggregated as they are measured. **To set a field role** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right to expand information about the field. 1. For **Role**, choose a role. You can choose a measure or a dimension. 1. (Optional) If your measure is inversely proportional (for example, the lower the number, the better), choose **Inverted measure**. This explains how to interpret and display the values in this field. ### Set field aggregations Setting field aggregations helps determine which function should or shouldn't be used when those fields are aggregated across multiple rows. You can set a default aggregation for a field, and a not allowed aggregation. A *default aggregation* is the aggregation that's applied when there's no explicit aggregation function mentioned or identified in a reader's question. For example, let's say one of your readers asks, "How many products were sold yesterday?" In this case, Q uses the field `Product ID`, which has a default aggregation of `count distinct`, to answer the question. Doing this results in a visual showing the distinct count of Product ID. *Not allowed aggregations* are aggregations that are excluded from being used on a field to answer a question. They're excluded even if the question specifically asks for a not allowed aggregation. For example, let's say you specify that the `Product ID` field should never be aggregated by `sum`. Even if one of your readers asks, "How many total products were sold yesterday?" `sum` isn't used to answer the question. If aggregate functions are incorrectly applied on a field, we recommend that you set not allowed aggregations for the field. **To set field aggregations** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right to expand information about the field. 1. For **Default aggregation**, choose the aggregation that you want to aggregate the field by default. You can aggregate measures by sum, average, max, and min. You can aggregate dimensions by count and count distinct. 1. (Optional) For **Not allowed aggregations**, choose an aggregation that you don't want to use. 1. (Optional) If you don't want to aggregate the field in a filter, choose **Never aggregate in a filter**. ### Specify how to format field values If you want to explain how to format the values in your fields, you can do so. For example, suppose that you have the field `Order Sales Amount`, which contains values that you want to format as U.S. dollars. In this case, you can explain how to format the values in the field as U.S. currency when used in answers. **To specify how to format field values** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right to expand information about the field. 1. For **Value format**, choose how you want to format the values in the field. ### Specify field semantic types A field *semantic type* is the type of information represented by the data in a field. For example, you might have a field that contains location data, currency data, age data, or Boolean data. You can specify a semantic type and additional semantic subtype for fields. Specifying these helps to understand the meaning of the data stored in your fields. Use the following procedure to specify field semantic types and subtypes. **To specify field semantic types** 1. Open the topic that you want to change. 1. In the topic, choose the **Data** tab. 1. In the **Fields** section, choose the down arrow at far right to expand information about the field. 1. For **Semantic type**, choose the kind of information the data represents. For measures, you can select duration, date part, location, boolean, currency, percentage, age, distance, and identifier types. For dimensions, you can select date part, location, Boolean, person, organization, and identifier types. 1. For **Semantic sub-type**, choose an option to further specify the kind of information the data represents. The options here depend on the semantic type that you chose and the role associated with the field. For a list of semantic types and their associated subtypes for measures and dimensions, see the following table. | Semantic Type | Semantic Subtype | Available for the Following | | --- | --- | --- | | Age | | Measures | | Boolean | | Dimensions and measures | | Currency | USD EUR GBP | Measures | | Date part | Day Week Month Year Quarter | Dimensions and measures | | Distance | Kilometer Meter Yard Foot | Measures | | Duration | Second Minute Hour Day | Measures | | Identifier | | Dimensions and measures | | Location | Zip code Country State City | Dimensions and measures | | Organization | | Dimensions | | Percentage | | Measures | | Person | | Dimensions | # Sharing Quick Sight topics Sharing topics | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | After you create a topic, you can share it with others in your organization. Sharing a topic allows your users to select the topic and ask questions about it in the search bar. After you share a topic with your users, you can assign permissions to them that specify who can change the topic. **To share a topic** 1. On the Quick start page, choose **Topics** at left. 1. On the **Topics** page that opens, open the topic that you want to share. 1. On the page that opens, choose **Share** at upper right. 1. On the **Share topic with users** page that opens, choose the user or users that you want to share the topic with. You can use the search bar to search for users by email address. 1. Choose either **Viewer** or **Co-owner** under the **Permission** column to assign permissions to your users. For more information about these permissions, see the following section, [Managing Amazon Quick Sight topic permissions](topics-sharing-permissions.md). 1. When you're finished selecting users, choose **Share**. # Managing Amazon Quick Sight topic permissions Manage topic permissions When you share your topics with others in your organization, you might want to control who can change them. To do this, specify which users are viewers and which are co-owners. *Viewers* can see the topic in the search bar when they select a topic from the list, but they can't change the topic data. *Co-owners* can see the topic in the search bar, and they can also change the topic. **To assign topic permissions to your users** 1. From the Quick start page, choose **Topics**. 1. On the **Topics** page that opens, open the topic that you want to manage permissions for. 1. On the topic page that opens, choose **Share** at upper right. 1. On the **Share topic with users** page that opens, choose **Manage topic access**. 1. On the **Manage topic permissions** page that opens, find the user that you want to manage access for, and then for **Permission**, choose one of the following options: + To allow a user to view and change the topic, choose **Co-Owner**. + To allow a user to view the topic only, choose **Viewer**. # Reviewing Quick Sight topic performance and feedback Reviewing topic performance and feedback | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | After you create a topic and share it with your users, you can review how that topic is performing. When someone uses your topic to ask a question or provides feedback on how well the response was, it's recorded on the topic's **Summary** and **User Activity** tabs. On the topic's **Summary** tab, you can view historical data for the number of questions asked over time, in time periods from seven days to a year. You can also see a distribution of questions that received positive, negative, or no feedback, and also questions that were unanswerable. On the **User Activity** tab, you can see a list of the questions that users asked and any positive or negative feedback and comments that they left. Reviewing this information can help you determine whether your topic is meeting your users' needs. For example, let's say you have a topic that's receiving a lot of negative feedback from your users. When you review your user activity, you notice that several users are leaving comments on a question that showed them the wrong data. In response, you examine the questions that they asked, and notice that they were using a term that you didn't anticipate. You decide to add that term as a synonym to the correct field in the topic. Over time, you notice an increase in positive feedback. ## Reviewing topic performance Use the following procedure to view how a topic is performing. **To view how a topic is performing** 1. On the Quick start page, choose **Topics** at left. 1. On the **Topics** page that opens, open the topic that you want to review. The topic opens and the **Statistics** section shows the topic's statistics. 1. (Optional) To change the amount of historical data shown in the chart, choose one of the following options: **7 days**, **30 days**, **90 days**, **120 days**, or **12 months**. 1. (Optional) To remove questions that were unanswerable from the data, clear **Include Unanswerable data**. 1. (Optional) To remove questions that didn't receive feedback from the data, clear **Include No feedback data**. ## Reviewing topic questions and feedback Use the following procedures to review a topic's questions and feedback. **To review topic questions and feedback** 1. On the Quick start page, choose **Topics**. 1. On the **Topics** page that opens, open the topic that you want to review feedback for. 1. On the topic's page that opens, choose the **User Activity** tab. The user activity for the topic is shown. At the top, you can see the total number of questions asked and the number of questions that were answerable and unanswerable. You can also see the percentage of questions that were rated positive and negative. Additionally, you can see the percentage of questions that were disambiguated. This means that someone entered a question and mapped one of the words in the question to a field in the topic. You can choose any of these statistics to filter the list of questions. 1. (Optional) To view a comment left by a user on a question, choose the down arrow at right of the question. The comment is shown at left. 1. (Optional) To view the fields used to respond to a question, choose the down arrow at right of the question. The fields used are shown at right. Choose a field name to edit its metadata. 1. (Optional) To view a question that was disambiguated, choose the down arrow at right of a question with a term highlighted in red. A description of the term and the field that was used to disambiguate it is shown. To add synonyms for the field, choose **Add synonyms**. 1. (Optional) To view how a question was responded to, choose **View** next to the question in the list. 1. (Optional) To filter the list of questions, choose **Filter by** at right, and then filter by one of the following options. + **See all questions** – This option removes all filters and shows all questions that a topic has received. + **Answerable** – This option filters the list of questions to those that were answerable. Answerable questions are questions that Q was able to respond to. + **Unanswerable** – This option filters the list of questions to those that were unanswerable. Unanswerable questions are questions that Q could not respond to. + **Disambiguated** – This option filters the list of questions to those that were disambiguated, meaning questions with terms that users manually mapped a field to. + **No feedback** – This option filters the list of questions to those that didn't receive feedback. + **Negative** – This option filters the list of questions to those that received negative feedback. + **Positive** – This option filters the list of questions to those that received positive feedback. + **No comments** – This option filters the list of questions to those that didn't receive comments from users. + **Has comments** – This option filters the list of questions to those that received comments from users. + **User** – This option filters the list of questions to those that were asked by a user with a specific user name that you enter. # Refreshing Quick Sight topic indexes Refreshing topic indexes | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick administrators and authors  | When you create a topic, Quick Sight creates, stores, and maintains an index with definitions for data in that topic. This index isn't exposed to Quick Sight authors. It's not a copy of the datasets included in a topic either. Metrics are not indexed. For example, measures stored as integers are not indexed. The topic index is an index of unique string values for fields included in a topic. This index is used to generate correct answers, provide autocomplete suggestions when someone asks a question, and suggest mappings of terms to columns or data values. To refresh a topic index, refresh the datasets in the topic. You can manually refresh all datasets in a topic or refresh an individual dataset. You can also view dataset refresh history to monitor past refreshes, and set a recurring refresh schedule for every dataset in the topic. For SPICE datasets, you can sync the topic index refresh schedule with the SPICE refresh schedule. For more information about setting SPICE refresh schedules, see [Refreshing a dataset on a schedule](refreshing-imported-data.md#schedule-data-refresh). **Note** Currently, hourly refresh schedules aren't supported. You can set a refresh schedule to refresh datasets in a topic up to once a day. We recommend that you update topic indexes regularly to ensure that the latest definitions and values are recorded. Updating a topic index takes approximately 15 to 30 minutes, depending on the number and size of datasets included in the topic. **To refresh a topic index** 1. On the Quick start page, choose **Topics**. 1. On the **Topics** page that opens, open the topic that you want to refresh. The topic opens to the **Summary** tab, which shows the datasets that are included in the topic at page bottom. It also shows when the last time the topic was refreshed at upper right. 1. Choose **Refreshed** at upper right to refresh the topic index, and then choose **Refresh data**. Doing this manually refreshes all datasets in the topic. For more information about refreshing individual datasets in a topic, see [Refreshing datasets in a Quick Sight topic](topics-data-refresh.md). # Work with Quick Sight topics using the Amazon Quick Sight APIs Using the Amazon Quick Sight APIs | | | --- | |  Applies to: Enterprise Edition  | | | | --- | |    Intended audience: Amazon Quick developers  | Use this section to learn how to work with Quick Sight topics using the Amazon Quick Sight command line interface (CLI). **Prerequisites** Before you begin, make sure that you have an AWS Identity and Access Management (IAM) role that grants the CLI user access to call the Quick Sight API operations. The following table shows which permissions must be added to the IAM policy to use specific API operations. To use all of the topic API operations, add all of the permissions listed in the table. | API operation | IAM policy | | --- | --- | | `CreateTopic` | `quicksight:CreateTopic` `quicksight:PassDataSet` | | `ListTopics` | `quicksight:ListTopics` | | `DescribeTopic` | `quicksight:DescribeTopic` | | `DescribeTopicPermissions` | `quicksight:DescribeTopicPermissions` | | `DescribeTopicRefresh` | `quicksight:DescribeTopicRefresh` | | `DeleteTopic` | `quicksight:DeleteTopic` | | `UpdateTopic` | `quicksight:UpdateTopic` `quicksight:PassDataSet` | | `UpdateTopicPermissions` | `quicksight:UpdateTopicPermissions` | | `CreateTopicRefreshSchedule` | `quicksight:CreateTopicRefreshSchedule` | | `ListTopicRefreshSchedules` | `quicksight:ListTopicRefreshSchedules` | | `DescribeTopicRefreshSchedule` | `quicksight:DescribeTopicRefreshSchedule` | | `UpdateTopicRefreshSchedule` | `quicksight:UpdateTopicRefreshSchedule` | | `DeleteTopicRefreshSchedule` | `quicksight:DeleteTopicRefreshSchedule` | | `BatchCreateTopicReviewedAnswer` | `quicksight:BatchCreateTopicReviewedAnswer` | | `BatchDeleteTopicReviewedAnswer` | `quicksight:BatchDeleteTopicReviewedAnswer` | | `ListTopicReviewedAnswers` | `quicksight:ListTopicReviewedAnswers` | The following example shows an IAM policy that allows a user to use the `ListTopics` API operation. ------ #### [ JSON ] **** ``` { "Version":"2012-10-17", "Statement": [ { "Effect": "Allow", "Action": [ "quicksight:ListTopics" ], "Resource": "*" } ] } ``` ------ After you configure the permissions to create Quick Sight topics with the Quick Sight APIs, use the following topics to create and work with Quick Sight topic APIs. **Topics** + [ # Work with Quick Sight topics using the Quick Sight APIs ](topic-cli-examples.md) + [ # Configure Quick Sight topic refresh schedules with the Quick Sight CLI ](topic-refresh-apis.md) + [ # Copy and migrate Quick Sight topics within and between AWS accounts ](topic-cli-walkthroughs.md) + [ # Create and modify reviewed answers in Quick Sight topics with the Quick Sight APIs ](topic-reviewed-answer-apis.md) # Work with Quick Sight topics using the Quick Sight APIs The following example creates a new topic. ``` aws quicksight create-topic --aws-account-id AWSACCOUNTID --topic-id TOPICID --topic TOPIC ``` You can also create a new topic by using a CLI skeleton file with the following command. For more information about CLI skeleton files, see [Using CLI skeleton files](https://docs.aws.amazon.com/quicksight/latest/developerguide/cli-skeletons.html) in the *Amazon Quick Sight Developer Guide*. ``` aws quicksight create-topic --cli-input-json file://createtopic.json ``` When you create a new topic, the dataset refresh configuration is not copied to the topic. To set a topic refresh schedule for your new topic, you can make a `create-topic-refresh-schedule` API call. For more information about configuring topic refresh schedules with the CLI, see [Configure Quick Sight topic refresh schedules with the Quick Sight CLI](topic-refresh-apis.md). After you create your first topic, you can update, delete, list, or request a summary of a topic. The following example updates a topic. ``` aws quicksight update-topic --aws-account-id AWSACCOUNTID --topic-id TOPICID --topic TOPIC ``` You can also update a topic by using a CLI skeleton file with the following command. For more information about CLI skeleton files, see [Using CLI skeleton files](https://docs.aws.amazon.com/quicksight/latest/developerguide/cli-skeletons.html) in the *Amazon Quick Sight Developer Guide*. ``` aws quicksight update-topic --cli-input-json file://updatetopic.json ``` The following example provides a list of all topics in a Quick account. ``` aws quicksight list-topics --aws-account-id AWSACCOUNTID ``` The following example deletes a topic. ``` aws quicksight delete-topic --aws-account-id AWSACCOUNTID --topic-id TOPICID ``` The following example provides information about how a topic was configured. ``` aws quicksight describe-topic --aws-account-id AWSACCOUNTID --topic-id TOPICID ``` The following command updates the permissions of a topic. ``` aws quicksight update-topic-permissions --aws-account-id AWSACCOUNTID --topic-id TOPICID --grant-permissions Principal=arn:aws:quicksight:us-east-1:AWSACCOUNTID:user/default/USERNAME,Actions=quicksight:DescribeTopic --revoke-permissions Principal=arn:aws:quicksight:us-east-1:AWSACCOUNTID:user/default/USERNAME,Actions=quicksight:DescribeTopic ``` Use the `grant-permissions` parameter to grant read and author permissions to Quick account users. To grant read permissions to an account user, enter the following value: `"quicksight:DescribeTopic"`. To grant permissions to an account user, enter the following values: + `"quicksight:DescribeTopic"` + `"quicksight:DescribeTopicRefresh"` + `"quicksight:ListTopicRefreshSchedules"` + `"quicksight:DescribeTopicRefreshSchedule"` + `"quicksight:DeleteTopic"` + `"quicksight:UpdateTopic"` + `"quicksight:CreateTopicRefreshSchedule"` + `"quicksight:DeleteTopicRefreshSchedule"` + `"quicksight:UpdateTopicRefreshSchedule"` + `"quicksight:DescribeTopicPermissions"` + `"quicksight:UpdateTopicPermissions"` The `RevokePermissions` parameter revokes all permissions granted to an account user. The following command describes all permissions from a topic. ``` aws quicksight describe-topic-permissions --aws-account-id AWSACCOUNTID --topic-id TOPICID ``` After you create a Quick Sight topic, you can use the Amazon Quick Sight APIs to [configure a topic refresh schedule](https://docs.aws.amazon.com/quicksuite/latest/userguide/topic-refresh-apis), [migrate Quick Sight topics within or between accounts](https://docs.aws.amazon.com/quicksuite/latest/userguide/topic-cli-walkthroughs), and [ create reviewed answers](https://docs.aws.amazon.com/quicksuite/latest/userguide/topic-reviewed-answer-apis). # Configure Quick Sight topic refresh schedules with the Quick Sight CLI Configure topic refresh schedules The following command creates a refresh schedule of a topic. ``` aws quicksight create-topic-refresh-schedule --aws-account-id AWSACCOUNTID --topic-id TOPICID --dataset-arn DATASETARN --refresh-schedule REFRESHSCHEDULE ``` After you create a refresh schedule for a topic, you can update, delete, list, or request a summary of the topic's refresh schedule. The following command updates the refresh schedule of a topic. ``` aws quicksight update-topic-refresh-schedule --aws-account-id AWSACCOUNTID --topic-id TOPICID --dataset-id DATASETID --refresh-schedule REFRESHSCHEDULE ``` The following example provides a list of all refresh schedules configured to a topic. ``` aws quicksight list-topic-refresh-schedules --aws-account-id AWSACCOUNTID --topic-id TOPICID ``` The following example deletes a topic refresh schedule. ``` aws quicksight delete-topic-refresh-schedule --aws-account-id AWSACCOUNTID --topic-id TOPICID --dataset-id DATASETID ``` The following example provides information about how a topic refresh schedule was configured. ``` aws quicksight describe-topic-refresh-schedule --aws-account-id AWSACCOUNTID --topic-id TOPICID --dataset-id DATASETID ``` # Copy and migrate Quick Sight topics within and between AWS accounts Migrate Quick Sight topics You can migrate your Quick Sight topics from one account to another with the Quick Sight command line interface (CLI). Instead of manually replicating the same topic across multiple dashboards, namespaces, or accounts, you can use the Quick Sight CLI to reuse the same topic repeatedly. This capability saves Quick Sight authors time and creates a standardized topic experience for dashboard readers across multiple dashboards. To migrate topics with the Quick Sight CLI, use the following procedure **To migrate a topic to another account** 1. First, identify the topic that you want to migrate. You can view a list of every topic in your Quick account with a `list-topics` API command. ``` aws quicksight list-topics --aws-account-id AWSACCOUNTID ``` 1. After you have a list of topics, locate the topic that you want to migrate and make a `describe-topic` call to receive a JSON structure of the topic's configuration. ``` aws quicksight describe-topic --aws-account-id AWSACCOUNTID --topic-id TOPICID ``` Following is an example of a `describe-topic` API response. ``` { "Status": 200, "TopicId": "TopicExample", "Arn": "string", "Topic": [ { "Name": "{}", "DataSets": [ { "DataSetArn": "{}", "DataSetName": "{}", "DataSetDescription": "{}", "DataAggregation": "{}", "Filters": [], "Columns": [], "CalculatedFields": [], "NamedEntities": [] } ] } ], "RequestId": "requestId" } ``` 1. Use the JSON response to create a skeleton file that you can input into a new `create-topic` call in your other Quick account. Before you make an API call with your skeleton file, make sure to change the AWS account ID and dataset ID in the skeleton file to match the AWS account ID and dataset ID that you are adding the new topic to. For more information about CLI skeleton files, see [Using CLI skeleton files](https://docs.aws.amazon.com/quicksight/latest/developerguide/cli-skeletons.html) in the *Amazon Quick Sight Developer Guide*. ``` aws quicksight create-topic --aws-account-id AWSACCOUNTID \ --cli-input-json file://./create-topic-cli-input.json ``` After you make a `create-topic` call to the Quick Sight API, the new topic appears in your account. To confirm that the new topic exists, make a `list-topics` call to the Quick Sight API. If the source topic that was duplicated contains verified answers, the answers are not migrated to the new topic. To see a list of all verified answers that are configured to the original topic, use a `describe-topic` API call. # Create and modify reviewed answers in Quick Sight topics with the Quick Sight APIs Create and modify reviewed answers with the Quick Sight APIs After you create a Quick Sight topic, you can use the Quick Sight APIs to create, list, update, and delete reiewed answers for topics. The command below batch creates up to 100 reviewed answers for a Quick Sight topic. ``` aws quicksight batch-create-topic-reviewed-answer \ --topic-id TOPICID \ --aws-account-id AWSACCOUNTID \ —answers ANSWERS ``` You can also batch create reviewed answers from a CLI skeleton file with the following command. For more information about CLI skeleton files, see [Using CLI skeleton files](https://docs.aws.amazon.com/quicksight/latest/developerguide/cli-skeletons.html) in the *Amazon Quick Sight Developer Guide*. ``` aws quicksight batch-create-topic-reviewed-answer \ --cli-input-json file://createTopicReviewedAnswer.json ``` The command below lists all reviewed answers in a Quick Sight topic. ``` aws quicksight list-topic-reviewed-answers \ --aws-account-id AWSACCOUNTID \ --topic-id TOPICID \ ``` The example below batch deletes up to 100 reviewed answers from a topic. ``` aws quicksight batch-delete-topic-reviewed-answer \ --topic-id TOPICID \ --aws-account-id AWSACCOUNTID \ —answer-ids: ["AnswerId1, AnswerId2…"] ``` You can also batch create topic reviewed answers form a CLI skeleton file with the following command. For more information about CLI skeleton files, see [Using CLI skeleton files](https://docs.aws.amazon.com/quicksight/latest/developerguide/cli-skeletons.html) in the *Amazon Quick Sight Developer Guide*. ``` aws quicksight batch-delete-topic-reviewed-answer \ --cli-input-json file://deleteTopicReviewedAnswer.json ``` To update a reviewed answer, delete the existing answer from the topic with the `batch-delete-topic-reviewed-answer` API. Then, use the `batch-create-topic-reviewed-answer` API to add the updated reviewed answer to the topic.