Practice Questions: Configure and Update a Workspace App (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Create and manage workspaces and assets
--> Configure and Update a Workspace App


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

What is the primary purpose of publishing a workspace app in Power BI?

A. To allow multiple developers to edit reports simultaneously
B. To provide a read-only, curated experience for report consumers
C. To improve dataset refresh performance
D. To apply row-level security to reports

Correct Answer: B

Explanation:
Workspace apps are designed for content consumption, not development. They provide a controlled, read-only experience for users, which is why they are preferred over direct report sharing for large audiences.


Question 2

Which workspace roles can publish or update a workspace app?

A. Viewer only
B. Contributor and Viewer
C. Member and Admin
D. Contributor and Member

Correct Answer: C

Explanation:
Only Members and Admins have permission to publish or update workspace apps. Contributors can create content but cannot manage apps.


Question 3

You update a report in a workspace, but users do not see the changes in the app. What must you do?

A. Refresh the dataset
B. Clear the app cache
C. Republish or update the app
D. Reassign user permissions

Correct Answer: C

Explanation:
Changes made in the workspace do not automatically appear in the app. You must explicitly update (republish) the app for consumers to see the changes.


Question 4

Which feature allows different users to see different content within the same workspace app?

A. Row-level security (RLS)
B. Dataset roles
C. App audiences
D. Visual-level filters

Correct Answer: C

Explanation:
App audiences control content visibility, allowing different groups to see different reports or dashboards without duplicating content.


Question 5

Which permission allows users of an app to build their own reports using the app’s semantic model?

A. Viewer permission
B. Allow reshare
C. Build permission
D. Admin permission

Correct Answer: C

Explanation:
Granting Build permission allows users to connect to the underlying semantic model for scenarios such as Analyze in Excel or creating new reports.


Question 6

What happens to users when a workspace app is updated?

A. They must be re-added to the app
B. Their bookmarks are deleted
C. They automatically see the updated content
D. Their permissions are reset

Correct Answer: C

Explanation:
After an app is updated, users retain access and permissions, and the updated content becomes available automatically without additional action.


Question 7

Which scenario is the best use case for a workspace app?

A. Collaborative report development
B. Testing new visuals
C. Distributing finalized reports to a large audience
D. Debugging DAX calculations

Correct Answer: C

Explanation:
Workspace apps are ideal for broad distribution of production-ready content, while workspaces remain the collaboration area for developers.


Question 8

Which of the following is true about workspace apps?

A. Users can edit reports in an app
B. Apps automatically update when workspace content changes
C. Apps provide a controlled navigation experience
D. Apps replace the need for workspaces

Correct Answer: C

Explanation:
Apps allow creators to control navigation, ordering, and visibility of content. They are read-only and require manual updates.


Question 9

You want to hide a report from most users but make it available to executives using the same app. What should you do?

A. Duplicate the report into a new workspace
B. Use row-level security
C. Create a separate app
D. Use app audiences

Correct Answer: D

Explanation:
App audiences allow selective visibility of content without duplication, making them ideal for role-based access to reports.


Question 10

What is the key difference between a workspace and a workspace app?

A. Workspaces support data refresh; apps do not
B. Workspaces are for collaboration; apps are for consumption
C. Apps allow editing; workspaces do not
D. Apps automatically apply security

Correct Answer: B

Explanation:
A workspace is where content is created and maintained, while a workspace app is the distribution layer for end users.


Exam Tip

If a question mentions:

  • Broad distribution
  • Read-only access
  • Controlled release
  • Audiences
  • Updating without disrupting users

➡️ The correct answer is almost always Workspace App.


Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Identify when a gateway is required (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Create and manage workspaces and assets
--> Identify when a gateway is required


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

You publish a Power BI report that imports data from an on-premises SQL Server and want to schedule daily refreshes in the Power BI service. What is required?

A. No additional configuration
B. A Power BI app
C. An on-premises data gateway
D. A premium capacity workspace

Correct Answer: C

Explanation:
Scheduled refresh from an on-premises data source requires a gateway to securely connect Power BI service to the local SQL Server.


Question 2

A dataset uses Azure SQL Database in Import mode with scheduled refresh enabled. Is a gateway required?

A. Yes, because scheduled refresh is enabled
B. Yes, because Import mode is used
C. No, because the data source is cloud-based
D. No, because the dataset is small

Correct Answer: C

Explanation:
Azure SQL Database is a cloud data source that Power BI can access directly, so no gateway is needed.


Question 3

You create a Power BI report using DirectQuery to an on-premises SQL Server. When users view the report in the Power BI service, what is required?

A. A gateway
B. A scheduled refresh
C. Import mode
D. Power BI Premium

Correct Answer: A

Explanation:
DirectQuery sends queries at report view time. A gateway is required for on-premises sources.


Question 4

Which scenario does NOT require a Power BI gateway?

A. Importing data from SharePoint Online
B. DirectQuery to an on-premises database
C. Refreshing an on-premises dataflow
D. Live connection to on-premises SSAS

Correct Answer: A

Explanation:
SharePoint Online is a cloud-based service and does not require a gateway.


Question 5

A report combines data from Azure Data Lake Storage and an on-premises file share. What is true?

A. No gateway is required because one source is cloud-based
B. A gateway is required for the on-premises source
C. A gateway is required for both sources
D. Gateways are not supported for mixed data sources

Correct Answer: B

Explanation:
Any on-premises data source used in the Power BI service requires a gateway, even in hybrid datasets.


Question 6

While working in Power BI Desktop, you connect to an on-premises SQL Server and refresh data locally. Is a gateway required?

A. Yes, always
B. Yes, if Import mode is used
C. No, gateways are only needed in the Power BI service
D. No, if DirectQuery is used

Correct Answer: C

Explanation:
Power BI Desktop connects directly to local data sources. Gateways are only required after publishing to the Power BI service.


Question 7

You want to refresh a Power BI dataflow that connects to an on-premises Oracle database. What is required?

A. Power BI Premium
B. A gateway
C. A paginated report
D. An app workspace

Correct Answer: B

Explanation:
Dataflows that use on-premises data sources require a gateway to refresh in the Power BI service.


Question 8

Which connection type always requires a gateway when the data source is on-premises?

A. Import with manual refresh
B. Import with scheduled refresh
C. DirectQuery
D. Both B and C

Correct Answer: D

Explanation:
Scheduled refresh and DirectQuery both require a gateway for on-premises data sources.


Question 9

A report uses a Live connection to an on-premises Analysis Services model. What is required?

A. A dataset refresh schedule
B. A gateway
C. Import mode
D. A certified dataset

Correct Answer: B

Explanation:
Live connections to on-premises Analysis Services require a gateway for real-time queries.


Question 10

Which factor is the most important when deciding if a gateway is required?

A. Dataset size
B. Data refresh frequency
C. Location of the data source
D. Number of report users

Correct Answer: C

Explanation:
Gateway requirements are based on whether the data source is accessible from the cloud or located on-premises.


Exam Tips

  • On-premises + Power BI service = Gateway
  • Cloud sources do not require gateways
  • DirectQuery and Live connections still require gateways
  • Desktop-only work never requires a gateway

Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Assign workspace roles (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Secure and govern Power BI items
--> Assign workspace roles


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

You need to allow a user to add and remove workspace users and change workspace settings.
Which workspace role should you assign?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: D. Admin

Explanation:
Only the Admin role can manage workspace access and modify workspace settings. Members can manage content but cannot manage users.


Question 2

A business user needs to view reports and dashboards but should not be able to modify or publish any content.
Which role is most appropriate?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: A. Viewer

Explanation:
The Viewer role provides read-only access, allowing users to consume and interact with content without making changes.


Question 3

Which workspace role allows a user to create and edit reports but not publish or update a workspace app?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: B. Contributor

Explanation:
Contributors can create and modify content but cannot publish apps or manage workspace access.


Question 4

A Power BI developer must publish a workspace app but should not be able to add or remove users from the workspace.
Which role should be assigned?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: C. Member

Explanation:
Members can publish and update workspace apps but cannot manage workspace access, which is restricted to Admins.


Question 5

Which role is required to delete a workspace?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: D. Admin

Explanation:
Only workspace Admins have permission to delete a workspace.


Question 6

You want to follow best practices for access management by minimizing ongoing maintenance when employees change roles.
What should you use when assigning workspace access?

A. Individual user accounts
B. Distribution lists
C. Azure AD security groups
D. Shareable links

Correct Answer: C. Azure AD security groups

Explanation:
Using Azure AD security groups simplifies governance by allowing access changes to be managed centrally.


Question 7

A user needs to configure scheduled refresh for a semantic model but should not manage workspace access.
Which role is the minimum required?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: C. Member

Explanation:
Configuring scheduled refresh requires Member or Admin permissions. Contributors cannot manage refresh settings.


Question 8

Which workspace role requires a Power BI Pro license when the workspace is not in Premium capacity?

A. Admin only
B. Contributor only
C. Viewer only
D. All workspace roles

Correct Answer: D. All workspace roles

Explanation:
When a workspace is not in Premium capacity, all users, including Viewers, require a Power BI Pro license to access content.


Question 9

Which statement correctly describes the difference between workspace roles and row-level security (RLS)?

A. Workspace roles control data visibility, RLS controls actions
B. Workspace roles control actions, RLS controls data visibility
C. Both control user actions only
D. Both control data visibility only

Correct Answer: B. Workspace roles control actions, RLS controls data visibility

Explanation:
Workspace roles define what users can do, while RLS defines what data users can see within reports.


Question 10

You are designing a production workspace and want report consumers to have the least privilege possible.
Which role should they be assigned?

A. Viewer
B. Contributor
C. Member
D. Admin

Correct Answer: A. Viewer

Explanation:
The Viewer role follows the principle of least privilege, granting read-only access appropriate for production consumers.


Exam Readiness Checklist

✔ Know all four workspace roles
✔ Understand capabilities vs limitations
✔ Apply least privilege principles
✔ Recognize Admin-only actions
✔ Distinguish workspace roles from RLS


Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Configure item-level access in Power BI (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Secure and govern Power BI items
--> Configure item-level access


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

You want business users to create their own reports using an existing semantic model, but you do not want them to edit the model. What should you grant them?

A. Workspace Viewer role
B. Workspace Contributor role
C. Build permission on the semantic model
D. Read permission on the report

Correct Answer: C

Explanation:
The Build permission allows users to create new reports using a semantic model without modifying it. Viewer access alone does not allow report creation, and Contributor access is broader than required.


Question 2

A user can view a dashboard but sees broken tiles that fail to load data. What is the most likely cause?

A. The dataset refresh failed
B. The user lacks Build permission
C. The user does not have access to the underlying report
D. The dashboard was shared incorrectly

Correct Answer: C

Explanation:
Dashboard tiles link back to underlying reports. If the user does not have access to those reports, the tiles will not display correctly—even if the dashboard itself is shared.


Question 3

Which permission allows a user to create a new report in Power BI Desktop using a published semantic model?

A. Read
B. Viewer
C. Contributor
D. Build

Correct Answer: D

Explanation:
Only the Build permission enables users to create new reports from an existing semantic model, including using Power BI Desktop or Analyze in Excel.


Question 4

You need to limit who can see specific reports within a Power BI app without creating multiple apps. What should you use?

A. Row-level security (RLS)
B. Workspace roles
C. App audiences
D. Dataset permissions

Correct Answer: C

Explanation:
App audiences provide item-level visibility within an app, allowing different user groups to see different reports or dashboards.


Question 5

Which statement best describes item-level access?

A. It controls what data rows users can see
B. It controls access to entire workspaces
C. It controls access to individual Power BI items
D. It replaces workspace roles

Correct Answer: C

Explanation:
Item-level access applies to individual items such as reports, dashboards, and datasets. It does not control row-level data access and does not replace workspace roles.


Question 6

A user has access to a report but cannot export data from it. What is the most likely explanation?

A. The dataset is using DirectQuery
B. The report is in a Premium workspace
C. Export permissions are restricted at the report or tenant level
D. The user lacks RLS permissions

Correct Answer: C

Explanation:
Export behavior is governed by item-level settings and tenant-level policies, not RLS or workspace type alone.


Question 7

When sharing a report, which permission must be explicitly granted if the user needs to reshare it with others?

A. Build
B. Viewer
C. Contributor
D. Reshare

Correct Answer: D

Explanation:
The Reshare permission must be explicitly enabled when sharing an item. Without it, users can view the report but cannot share it further.


Question 8

Which scenario requires item-level access instead of workspace roles?

A. Granting full control of all assets
B. Managing dataset refresh schedules
C. Allowing users to view only specific reports in a workspace
D. Enabling paginated report creation

Correct Answer: C

Explanation:
Item-level access allows fine-grained control over individual assets, making it ideal when users should only see specific reports.


Question 9

How does item-level access differ from row-level security (RLS)?

A. Item-level access controls data rows
B. RLS controls report visibility
C. Item-level access controls content access; RLS controls data visibility
D. They serve the same purpose

Correct Answer: C

Explanation:
Item-level access determines whether a user can open or interact with content, while RLS limits the data shown within that content.


Question 10

What is the recommended best practice when assigning item-level access at scale?

A. Assign permissions to individual users
B. Use workspace roles only
C. Use Azure AD security groups
D. Share reports anonymously

Correct Answer: C

Explanation:
Using Azure AD security groups improves scalability, simplifies maintenance, and aligns with enterprise governance best practices.


Exam Readiness Tip

If you can confidently answer questions about:

  • Build vs Read vs Reshare
  • Dashboards vs reports vs datasets
  • Item-level access vs workspace roles vs RLS

…you are in excellent shape for PL-300 questions in this domain.


Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Configure Access to Semantic Models (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Secure and govern Power BI items
--> Configure access to semantic models


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

A user can view reports in a workspace but cannot create a new report using the existing semantic model. What is the most likely reason?

A. The user does not have Read permission on the semantic model
B. The user does not have Build permission on the semantic model
C. The user is not assigned a Row-Level Security role
D. The semantic model is not endorsed

Correct Answer: B

Explanation:
Creating new reports from a semantic model requires Build permission. A user can still view reports without Build permission, which makes this a common exam scenario.


Question 2

Which workspace role allows a user to edit semantic models and manage permissions?

A. Viewer
B. Contributor
C. Member
D. App user

Correct Answer: C

Explanation:
Members can publish, update, and manage semantic models, including assigning permissions. Contributors can edit content but cannot manage access.


Question 3

You want business users to create their own reports while preventing them from modifying the semantic model. What is the best approach?

A. Assign users the Viewer role and grant Build permission on the semantic model
B. Assign users the Contributor role
C. Assign users the Admin role
D. Publish the reports through a Power BI App only

Correct Answer: A

Explanation:
Granting Viewer role + Build permission enables self-service report creation without allowing model changes—this is a best practice and frequently tested.


Question 4

Where is Row-Level Security (RLS) enforced?

A. At the report level
B. At the dashboard level
C. At the semantic model level
D. At the workspace level

Correct Answer: C

Explanation:
RLS is defined in Power BI Desktop and enforced at the semantic model level, applying to all reports that use the model.


Question 5

Which DAX function is commonly used to implement dynamic Row-Level Security?

A. SELECTEDVALUE()
B. USERELATIONSHIP()
C. USERPRINCIPALNAME()
D. LOOKUPVALUE()

Correct Answer: C

Explanation:
USERPRINCIPALNAME() returns the logged-in user’s email or UPN and is commonly used in dynamic RLS filters.


Question 6

A user with Viewer access can see a report but receives an error when using Analyze in Excel. What is the most likely issue?

A. The user is not licensed for Power BI
B. The semantic model is not certified
C. The user does not have Build permission
D. RLS is incorrectly configured

Correct Answer: C

Explanation:
Analyze in Excel requires Build permission on the semantic model. Viewer role alone is insufficient.


Question 7

Which permission allows a user to share a semantic model with others?

A. Read
B. Build
C. Reshare
D. Admin

Correct Answer: C

Explanation:
The Reshare permission explicitly allows users to share the semantic model with other users or groups.


Question 8

What is the primary purpose of certifying a semantic model?

A. To apply Row-Level Security automatically
B. To improve query performance
C. To indicate the model is an approved and trusted data source
D. To allow external tool access

Correct Answer: C

Explanation:
Certification signals that a semantic model is officially approved and governed, helping users identify trusted data sources.


Question 9

Which approach is recommended for managing access to semantic models at scale?

A. Assign permissions to individual users
B. Use Microsoft Entra ID (Azure AD) security groups
C. Share semantic models directly from Power BI Desktop
D. Grant Admin role to all analysts

Correct Answer: B

Explanation:
Using security groups simplifies access management, supports scalability, and aligns with governance best practices.


Question 10

A report is published using a semantic model that has RLS enabled. A user accesses the report through a Power BI App. What happens?

A. RLS is ignored when using apps
B. RLS must be reconfigured for the app
C. RLS is enforced automatically
D. Only static RLS is applied

Correct Answer: C

Explanation:
Row-Level Security is always enforced at the semantic model level, regardless of whether content is accessed via a workspace, report, or app.


Final Exam Tips

  • Build permission is the most frequently tested concept
  • Viewer + Build is a common least-privilege design pattern
  • RLS always applies at the semantic model level
  • Certification is about trust and governance, not security
  • Apps do not bypass semantic model security

Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Implement Row-Level Security Roles (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections:
Manage and secure Power BI (15–20%)
--> Secure and govern Power BI items
--> Implement row-level security roles


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

Where are Row-Level Security roles and filters created?

A. In the Power BI Service
B. In Power BI Desktop
C. In Microsoft Entra ID
D. In Power BI Apps

Correct Answer: B

Explanation:
RLS roles and DAX filters are created in Power BI Desktop. Users and groups are assigned to those roles later in the Power BI Service.


Question 2

Which DAX function is most commonly used to implement dynamic RLS?

A. USERELATIONSHIP()
B. USERNAME()
C. USERPRINCIPALNAME()
D. SELECTEDVALUE()

Correct Answer: C

Explanation:
USERPRINCIPALNAME() returns the logged-in user’s email/UPN and is the most commonly used function for dynamic RLS scenarios.


Question 3

A single semantic model must filter sales data so that users only see rows matching their email address. What is the best approach?

A. Create one role per user
B. Create static RLS roles by region
C. Use dynamic RLS with a user-mapping table
D. Use Object-Level Security

Correct Answer: C

Explanation:
Dynamic RLS with a user-to-dimension mapping table scales efficiently and avoids creating many static roles.


Question 4

What happens if a user belongs to multiple RLS roles?

A. Access is denied
B. Only the most restrictive role is applied
C. The union of all role filters is applied
D. The first role alphabetically is applied

Correct Answer: C

Explanation:
Power BI applies the union of RLS role filters, meaning users see data allowed by any role they belong to.


Question 5

Which statement about Row-Level Security behavior is correct?

A. RLS is applied at the report level
B. RLS applies only to dashboards
C. RLS is enforced at the semantic model level
D. RLS must be reconfigured for each report

Correct Answer: C

Explanation:
RLS is enforced at the semantic model level and automatically applies to all reports and apps using that model.


Question 6

You test RLS using View as role in Power BI Desktop. What does this feature do?

A. Permanently applies RLS to the model
B. Bypasses RLS for the model author
C. Simulates how the report appears for a role
D. Assigns users to roles automatically

Correct Answer: C

Explanation:
View as allows you to simulate role behavior to validate RLS logic before publishing.


Question 7

Which type of RLS is least scalable in enterprise environments?

A. Dynamic RLS
B. RLS using USERPRINCIPALNAME()
C. Static RLS with hard-coded values
D. Group-based RLS

Correct Answer: C

Explanation:
Static RLS requires separate roles for each data segment, making it difficult to maintain at scale.


Question 8

A user accesses a report through a Power BI App. How does RLS behave?

A. RLS is ignored
B. RLS must be redefined in the app
C. RLS is enforced automatically
D. Only static RLS is enforced

Correct Answer: C

Explanation:
RLS is always enforced at the semantic model level, including when content is accessed through apps.


Question 9

Which security feature should be used if you need to hide entire columns or tables from certain users?

A. Row-Level Security
B. Workspace roles
C. Object-Level Security
D. Build permission

Correct Answer: C

Explanation:
RLS controls rows only. Object-Level Security (OLS) is used to hide tables or columns.


Question 10

Which best practice is recommended when assigning users to RLS roles?

A. Assign individual users directly
B. Assign workspace Admins only
C. Assign Microsoft Entra ID security groups
D. Assign report-level permissions

Correct Answer: C

Explanation:
Using security groups improves scalability, governance, and ease of maintenance.


Final PL-300 Exam Reminders

  • RLS controls data visibility, not report access
  • Dynamic RLS is heavily tested
  • RLS applies everywhere the semantic model is used
  • Users see the union of multiple roles
  • RLS is defined in Desktop, enforced in the Service

Go back to the PL-300 Exam Prep Hub main page

Practice Questions: Configure Row-Level Security Group Membership (PL-300 Exam Prep)

This post is a part of the PL-300: Microsoft Power BI Data Analyst Exam Prep Hub; and this topic falls under these sections: 
Manage and secure Power BI (15–20%)
--> Secure and govern Power BI items
--> Configure row-level security group membership


Below are 10 practice questions (with answers and explanations) for this topic of the exam.
There are also 2 practice tests for the PL-300 exam with 60 questions each (with answers) available on the hub.

Practice Questions


Question 1

Where are security groups assigned to RLS roles?

A. Power BI Desktop
B. Power BI Service
C. Microsoft Entra ID only
D. Power BI App settings

Correct Answer: B

Explanation:
RLS roles and filters are created in Power BI Desktop, but users and security groups are assigned to roles in the Power BI Service after the model is published.


Question 2

Which approach is considered a best practice for managing RLS membership at scale?

A. Assign individual users to each role
B. Create one role per user
C. Assign Microsoft Entra ID security groups to roles
D. Use workspace Admin access

Correct Answer: C

Explanation:
Using Entra ID security groups simplifies administration, supports scalability, and aligns with enterprise security standards.


Question 3

What happens when a user is added to an Entra ID security group that is already assigned to an RLS role?

A. The semantic model must be republished
B. The role must be recreated
C. The user automatically inherits the RLS permissions
D. The user must be manually added in Power BI

Correct Answer: C

Explanation:
Group-based RLS automatically applies to all members of the group without changes to the model or Power BI configuration.


Question 4

Which type of group is recommended for RLS role membership?

A. Distribution list
B. Microsoft 365 group
C. Entra ID security group
D. Power BI workspace group

Correct Answer: C

Explanation:
Entra ID security groups are designed for access control and are the preferred option for RLS scenarios.


Question 5

A user belongs to two security groups, each assigned to a different RLS role. How is access determined?

A. The most restrictive role applies
B. The first role applied alphabetically applies
C. Access is denied
D. The union of both roles applies

Correct Answer: D

Explanation:
Power BI applies the union of all RLS roles a user belongs to, allowing access to any data permitted by either role.


Question 6

Which action requires updating Microsoft Entra ID, not Power BI?

A. Modifying a DAX RLS filter
B. Creating a new RLS role
C. Adding a user to an RLS role via group membership
D. Testing RLS with View as

Correct Answer: C

Explanation:
User membership in security groups is managed in Entra ID, not in Power BI.


Question 7

Which statement about testing group-based RLS is correct?

A. Group membership can be fully tested in Power BI Desktop
B. Group membership is evaluated only in the Power BI Service
C. RLS does not apply to groups
D. Groups bypass dynamic RLS

Correct Answer: B

Explanation:
Power BI Desktop can test role logic, but actual group membership is evaluated only in the Power BI Service.


Question 8

Why is group-based RLS preferred over assigning individual users?

A. It improves report performance
B. It hides tables and columns
C. It reduces the need to update Power BI when users change roles
D. It removes the need for DAX filters

Correct Answer: C

Explanation:
Group-based RLS allows access changes to be managed centrally without modifying Power BI roles or republishing models.


Question 9

Which security concept is often confused with RLS group membership but serves a different purpose?

A. Build permission
B. Workspace roles
C. Object-Level Security
D. All of the above

Correct Answer: D

Explanation:
All listed options are different security mechanisms that control content access or structure, not row-level data visibility.


Question 10

What is the primary role of Power BI in a group-based RLS solution?

A. Managing group membership
B. Authenticating users
C. Enforcing data filters defined in RLS roles
D. Creating security groups

Correct Answer: C

Explanation:
Power BI enforces RLS filters at query time, while identity and group membership are managed externally in Entra ID.


Final PL-300 Exam Reminders

  • Use Entra ID security groups for RLS membership
  • Assign groups in the Power BI Service
  • RLS role logic lives in Power BI Desktop
  • Users see the union of all assigned roles
  • Group membership changes do not require republishing

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Glossary – 100 “AI” Terms

Below is a glossary that includes 100 common “AI (Artificial Intelligence)” terms and phrases in alphabetical order. Enjoy!

TermDefinition & Example
 AccuracyPercentage of correct predictions. Example: 92% accuracy.
 AgentAI entity performing tasks autonomously. Example: Task-planning agent.
 AI AlignmentEnsuring AI goals match human values. Example: Safe AI systems.
 AI BiasSystematic unfairness in AI outcomes. Example: Biased hiring models.
 AlgorithmA set of rules used to train models. Example: Decision tree algorithm.
 Artificial General Intelligence (AGI)Hypothetical AI with human-level intelligence. Example: Broad reasoning across tasks.
 Artificial Intelligence (AI)Systems that perform tasks requiring human-like intelligence. Example: Chatbots answering questions.
 Artificial Neural Network (ANN)A network of interconnected artificial neurons. Example: Credit scoring models.
 Attention MechanismFocuses model on relevant input parts. Example: Language translation.
 AUCArea under ROC curve. Example: Model comparison.
 AutoMLAutomated model selection and tuning. Example: Auto-generated models.
 Autonomous SystemAI operating with minimal human input. Example: Self-driving cars.
 BackpropagationMethod to update neural network weights. Example: Deep learning training.
 BatchSubset of data processed at once. Example: Batch size of 32.
 Batch InferencePredictions made in bulk. Example: Nightly scoring jobs.
 Bias (Model Bias)Error from oversimplified assumptions. Example: Linear model on non-linear data.
 Bias–Variance TradeoffBalance between bias and variance. Example: Choosing model complexity.
 Black Box ModelModel with opaque internal logic. Example: Deep neural networks.
 ClassificationPredicting categorical outcomes. Example: Email spam classification.
 ClusteringGrouping similar data points. Example: Customer segmentation.
 Computer VisionAI for interpreting images and video. Example: Facial recognition.
 Concept DriftChanges in underlying relationships. Example: Fraud patterns evolving.
 Confusion MatrixTable evaluating classification results. Example: True positives vs false positives.
 Data AugmentationExpanding data via transformations. Example: Image rotation.
 Data DriftChanges in input data distribution. Example: New user demographics.
 Data LeakageUsing future information in training. Example: Including test labels.
 Decision TreeTree-based decision model. Example: Loan approval logic.
 Deep LearningML using multi-layer neural networks. Example: Image recognition.
 Dimensionality ReductionReducing number of features. Example: PCA for visualization.
 Edge AIAI running on local devices. Example: Smart cameras.
 EmbeddingNumerical representation of data. Example: Word embeddings.
 Ensemble ModelCombining multiple models. Example: Random forest.
 EpochOne full pass through training data. Example: 50 training epochs.
 Ethics in AIMoral considerations in AI use. Example: Avoiding bias.
 Explainable AI (XAI)Making AI decisions understandable. Example: Feature importance charts.
 F1 ScoreBalance of precision and recall. Example: Imbalanced datasets.
 FairnessEquitable AI outcomes across groups. Example: Equal approval rates.
 FeatureAn input variable for a model. Example: Customer age.
 Feature EngineeringCreating or transforming features to improve models. Example: Calculating customer tenure.
 Federated LearningTraining models across decentralized data. Example: Mobile keyboard predictions.
 Few-Shot LearningLearning from few examples. Example: Custom classification with few samples.
 Fine-TuningFurther training a pre-trained model. Example: Custom chatbot training.
 GeneralizationModel’s ability to perform on new data. Example: Accurate predictions on unseen data.
 Generative AIAI that creates new content. Example: Text or image generation.
 Gradient BoostingSequentially improving weak models. Example: XGBoost.
 Gradient DescentOptimization technique adjusting weights iteratively. Example: Training neural networks.
 HallucinationModel generates incorrect information. Example: False factual claims.
 HyperparameterConfiguration set before training. Example: Learning rate.
 InferenceUsing a trained model to predict. Example: Real-time recommendations.
 K-MeansClustering algorithm. Example: Market segmentation.
 Knowledge GraphGraph-based representation of knowledge. Example: Search engines.
 LabelThe correct output for supervised learning. Example: “Fraud” or “Not Fraud”.
 Large Language Model (LLM)AI trained on massive text corpora. Example: ChatGPT.
 Loss FunctionMeasures model error during training. Example: Mean squared error.
 Machine Learning (ML)AI that learns patterns from data without explicit programming. Example: Spam email detection.
 MLOpsPractices for managing ML lifecycle. Example: CI/CD for models.
 ModelA trained mathematical representation of patterns. Example: Logistic regression model.
 Model DeploymentMaking a model available for use. Example: API-based predictions.
 Model DriftModel performance degradation over time. Example: Changing customer behavior.
 Model InterpretabilityAbility to understand model behavior. Example: Decision tree visualization.
 Model VersioningTracking model changes. Example: v1 vs v2 models.
 MonitoringTracking model performance in production. Example: Accuracy alerts.
 Multimodal AIAI handling multiple data types. Example: Text + image models.
 Naive BayesProbabilistic classification algorithm. Example: Spam filtering.
 Natural Language Processing (NLP)AI for understanding human language. Example: Sentiment analysis.
 Neural NetworkModel inspired by the human brain’s structure. Example: Handwritten digit recognition.
 OptimizationProcess of minimizing loss. Example: Gradient descent.
 OverfittingModel learns noise instead of patterns. Example: Perfect training accuracy, poor test accuracy.
 PipelineAutomated ML workflow. Example: Training-to-deployment flow.
 PrecisionCorrect positive predictions rate. Example: Fraud detection precision.
 Pretrained ModelModel trained on general data. Example: GPT models.
 Principal Component Analysis (PCA)Technique for dimensionality reduction. Example: Compressing high-dimensional data.
 PrivacyProtecting personal data. Example: Anonymizing training data.
 PromptInput instruction for generative models. Example: “Summarize this text.”
 Prompt EngineeringCrafting effective prompts. Example: Improving LLM responses.
 Random ForestEnsemble of decision trees. Example: Classification tasks.
 Real-Time InferenceImmediate predictions on live data. Example: Fraud detection.
 RecallAbility to find all positives. Example: Cancer detection.
 RegressionPredicting numeric values. Example: Sales forecasting.
 Reinforcement LearningLearning through rewards and penalties. Example: Game-playing AI.
 ReproducibilityAbility to recreate results. Example: Fixed random seeds.
 RoboticsAI applied to physical machines. Example: Warehouse robots.
 ROC CurvePerformance visualization for classifiers. Example: Threshold analysis.
 Semi-Supervised LearningMix of labeled and unlabeled data. Example: Image classification with limited labels.
 Speech RecognitionConverting speech to text. Example: Voice assistants.
 Supervised LearningLearning using labeled data. Example: Predicting house prices from known values.
 Support Vector Machine (SVM)Algorithm separating data with margins. Example: Text classification.
 Synthetic DataArtificially generated data. Example: Privacy-safe training.
 Test DataData used to evaluate model performance. Example: Held-out validation dataset.
 ThresholdCutoff for classification decisions. Example: Probability > 0.7.
 TokenSmallest unit of text processed by models. Example: Words or subwords.
 Training DataData used to teach a model. Example: Historical sales records.
 Transfer LearningReusing knowledge from another task. Example: Image model reused for medical scans.
 TransformerNeural architecture for sequence data. Example: Language translation models.
 UnderfittingModel too simple to capture patterns. Example: High error on all datasets.
 Unsupervised LearningLearning from unlabeled data. Example: Customer clustering.
 Validation DataData used to tune model parameters. Example: Hyperparameter selection.
 VarianceError from sensitivity to data fluctuations. Example: Highly complex model.
 XGBoostOptimized gradient boosting algorithm. Example: Kaggle competitions.
 Zero-Shot LearningPerforming tasks without examples. Example: Classifying unseen labels.

Please share your suggestions for any terms that should be added.

Glossary – 100 “Data Engineering” Terms

Below is a glossary that includes 100 common “Data Engineering” terms and phrases in alphabetical order. Enjoy!

TermDefinition & Example
Access ControlManaging who can access data. Example: Role-based permissions.
At-Least-Once ProcessingData may be processed more than once. Example: Duplicate-safe pipelines.
At-Most-Once ProcessingData processed zero or one time. Example: No retries on failure.
BackfillProcessing historical data. Example: Reloading last year’s data.
Batch ProcessingProcessing data in scheduled chunks. Example: Daily sales aggregation.
Blue-Green DeploymentDeployment strategy minimizing downtime. Example: Switching pipeline versions.
Canary ReleaseGradual rollout to detect issues. Example: New pipeline tested on 5% of data.
Change Data Capture (CDC)Capturing database changes. Example: Streaming updates from OLTP DB.
CheckpointingSaving progress during processing. Example: Spark streaming checkpoints.
Cloud StorageScalable remote data storage. Example: Azure Data Lake Storage.
Cold StorageLow-cost storage for infrequent access. Example: Archived logs.
Columnar StorageData stored by column instead of row. Example: Parquet files.
CompressionReducing data size. Example: Gzip-compressed files.
Compute EngineSystem performing data processing. Example: Spark cluster.
Consumption LayerData prepared for analytics. Example: Gold layer.
Cost OptimizationReducing infrastructure costs. Example: Query optimization.
Curated LayerCleaned and transformed data. Example: Silver layer.
DAG (Directed Acyclic Graph)Workflow structure with dependencies. Example: Airflow pipeline.
Data CatalogSearchable inventory of data assets. Example: Azure Purview.
Data ContractAgreement defining data structure and expectations. Example: Producer guarantees column names and types.
Data EngineeringThe practice of designing, building, and maintaining data systems. Example: Creating pipelines that feed analytics dashboards.
Data GovernancePolicies for data management and usage. Example: Access control rules.
Data IngestionCollecting data from source systems. Example: Ingesting API data hourly.
Data LakeCentralized storage for raw data. Example: S3-based data lake.
Data LatencyTime delay in data availability. Example: 5-minute pipeline delay.
Data LineageTracking data flow from source to output. Example: Source-to-dashboard trace.
Data MartSubset of warehouse for specific use. Example: Finance data mart.
Data MaskingObscuring sensitive data. Example: Masked credit card numbers.
Data MeshDomain-oriented decentralized data ownership. Example: Teams own their data products.
Data ModelingDesigning data structures for usage. Example: Star schema design.
Data ObservabilityMonitoring data health and pipelines. Example: Freshness alerts.
Data Partition PruningSkipping irrelevant partitions. Example: Querying one date only.
Data PipelineAn automated process that moves and transforms data. Example: Nightly ETL job from CRM to warehouse.
Data PlatformIntegrated set of data tools. Example: End-to-end analytics stack.
Data ProductA dataset treated as a product. Example: Curated customer table.
Data ProfilingAnalyzing data characteristics. Example: Value distributions.
Data QualityAccuracy, completeness, and reliability of data. Example: No duplicate records.
Data ReplayReprocessing historical events. Example: Rebuilding aggregates from logs.
Data RetentionRules for data lifespan. Example: Delete logs after 1 year.
Data SecurityProtecting data from unauthorized access. Example: Encryption at rest.
Data SerializationConverting data for storage or transport. Example: Avro encoding.
Data SinkThe destination where data is stored. Example: Data warehouse.
Data SourceThe origin of data. Example: ERP system, SaaS application.
Data ValidationEnsuring data meets expectations. Example: Null checks.
Data VersioningTracking dataset changes. Example: Snapshot tables.
Data WarehouseOptimized storage for analytics queries. Example: Azure Synapse Analytics.
Dead Letter Queue (DLQ)Storage for failed records. Example: Invalid messages routed for review.
Dimension TableTable storing descriptive attributes. Example: Customer details.
ELTExtract, Load, Transform approach. Example: Transforming data inside Snowflake.
ETLExtract, Transform, Load process. Example: Cleaning data before loading into a database.
Event TimeTimestamp when event occurred. Example: User click time.
Event-Driven ArchitectureSystems reacting to events in real time. Example: Trigger pipeline on file arrival.
Exactly-Once ProcessingEnsuring data is processed only once. Example: Preventing duplicate events.
Fact TableTable storing quantitative measures. Example: Order transactions.
Fault ToleranceSystem resilience to failures. Example: Node failure recovery.
File FormatHow data is stored on disk. Example: Parquet, CSV.
Foreign KeyField linking tables together. Example: CustomerID in orders table.
Full LoadReloading all data. Example: Initial table population.
High AvailabilitySystem uptime and reliability. Example: Multi-zone deployment.
Hot StorageHigh-performance storage for frequent access. Example: Real-time tables.
IdempotencyAbility to rerun pipelines safely. Example: Reprocessing without duplicates.
Incremental LoadLoading only new or changed data. Example: CDC-based ingestion.
IndexingCreating structures to speed queries. Example: Index on order date.
Infrastructure as Code (IaC)Managing infrastructure via code. Example: Terraform scripts.
LakehouseHybrid of data lake and warehouse. Example: Databricks Lakehouse.
Late-Arriving DataData that arrives after expected time. Example: Delayed event logs.
LoggingRecording system events. Example: Job execution logs.
Message QueueBuffer for asynchronous data transfer. Example: Kafka topic for events.
MetadataData about data. Example: Table definitions and lineage.
MetricsQuantitative indicators of performance. Example: Rows processed per run.
OrchestrationCoordinating pipeline execution. Example: DAG scheduling.
PartitioningDividing data for performance. Example: Partitioning by date.
Personally Identifiable Information (PII)Data identifying individuals. Example: Email addresses.
Pipeline MonitoringTracking pipeline execution status. Example: Failure notifications.
Primary KeyUnique identifier for a record. Example: CustomerID.
Processing TimeTimestamp when data is processed. Example: Ingestion time.
Query OptimizationImproving query efficiency. Example: Predicate pushdown.
Raw LayerStorage of unprocessed data. Example: Bronze layer.
Real-Time DataData available with minimal latency. Example: Live dashboard updates.
Retry LogicAutomatic reruns on failure. Example: Retry failed ingestion job.
ScalabilityAbility to handle growing workloads. Example: Auto-scaling clusters.
SchedulerTool managing execution timing. Example: Cron, Airflow.
SchemaThe structure of a dataset. Example: Table columns and data types.
Schema EvolutionHandling schema changes over time. Example: Adding new columns safely.
Secrets ManagementSecure handling of credentials. Example: Key Vault for passwords.
Semi-Structured DataData with flexible schema. Example: JSON, Parquet.
ServerlessInfrastructure managed by provider. Example: Serverless SQL pools.
Serving LayerLayer optimized for consumption. Example: BI-ready tables.
ShardingDistributing data across nodes. Example: User data split across servers.
Snowflake SchemaNormalized version of star schema. Example: Product broken into sub-dimensions.
Star SchemaFact table surrounded by dimensions. Example: Sales fact with date dimension.
Stream ProcessingProcessing data in real time. Example: Clickstream event processing.
Structured DataData with a fixed schema. Example: SQL tables.
Technical DebtLong-term cost of quick fixes. Example: Hardcoded transformations.
ThroughputAmount of data processed per unit time. Example: Records per second.
Transformation LayerLayer where business logic is applied. Example: dbt models.
Unstructured DataData without a predefined structure. Example: Images, PDFs.
WatermarkMarker for processed data. Example: Last processed timestamp.
WindowingGrouping stream data by time windows. Example: 5-minute aggregations.
Workload IsolationSeparating workloads to avoid contention. Example: Dedicated compute pools.

Please share your suggestions for any terms that should be added.

Glossary – 100 “Data Analysis” Terms

Below is a glossary that includes 100 common “Data Analysis” terms and phrases in alphabetical order. Enjoy!

TermDefinition & Example
A/B TestComparing two variations to measure impact. Example: Two webpage layouts.
Actionable InsightAn insight that leads to a clear decision. Example: Improve onboarding experience.
Ad Hoc AnalysisOne-off analysis for a specific question. Example: Investigating a sudden sales dip.
AggregationSummarizing data using functions like sum or average. Example: Total revenue by region.
Analytical MaturityOrganization’s capability to use data effectively. Example: Moving from descriptive to predictive analytics.
Bar ChartA chart comparing categories. Example: Sales by region.
BaselineA reference point for comparison. Example: Last year’s sales used as baseline.
BenchmarkA standard used to compare performance. Example: Industry average churn rate.
BiasSystematic error in data or analysis. Example: Surveying only active users.
Business QuestionA decision-focused question data aims to answer. Example: Which products drive profit?
CausationA relationship where one variable causes another. Example: Price cuts causing sales growth.
Confidence IntervalRange likely containing a true value. Example: 95% CI for average sales.
CorrelationA statistical relationship between variables. Example: Sales and marketing spend.
Cumulative TotalA running total over time. Example: Year-to-date revenue.
DashboardA visual collection of key metrics. Example: Executive sales dashboard.
DataRaw facts or measurements collected for analysis. Example: Sales transactions, sensor readings, survey responses.
Data AnomalyUnexpected or unusual data pattern. Example: Sudden spike in user signups.
Data CleaningCorrecting or removing inaccurate data. Example: Fixing misspelled country names.
Data ConsistencyUniform representation across datasets. Example: Same currency used everywhere.
Data GovernancePolicies ensuring data quality, security, and usage. Example: Defined data ownership roles.
Data ImputationReplacing missing values with estimated ones. Example: Filling null ages with the median.
Data LineageTracking data origin and transformations. Example: Tracing metrics back to source systems.
Data LiteracyAbility to read, understand, and use data. Example: Interpreting charts correctly.
Data ModelThe structure defining how data tables relate. Example: Star schema.
Data PipelineAutomated flow of data from source to destination. Example: Daily ingestion job.
Data ProfilingAnalyzing data characteristics. Example: Checking null percentages.
Data QualityThe accuracy, completeness, and reliability of data. Example: Valid dates and consistent formats.
Data RefreshUpdating data with the latest values. Example: Nightly refresh.
Data Refresh FrequencyHow often data is updated. Example: Hourly vs. daily refresh.
Data SkewnessDegree of asymmetry in data distribution. Example: Income data skewed to the right.
Data SourceThe origin of data. Example: SQL database, API.
Data StorytellingCommunicating insights using narrative and visuals. Example: Executive-ready presentation.
Data TransformationModifying data to improve usability or consistency. Example: Converting text dates to date data types.
Data ValidationEnsuring data meets rules and expectations. Example: No negative quantities.
Data WranglingTransforming raw data into a usable format. Example: Reshaping columns for analysis.
DatasetA structured collection of related data. Example: A table of customer orders with dates, amounts, and regions.
Derived MetricA metric calculated from other metrics. Example: Profit margin = Profit / Revenue.
Descriptive AnalyticsAnalysis that explains what happened. Example: Last quarter’s sales summary.
Diagnostic AnalyticsAnalysis that explains why something happened. Example: Revenue drop due to fewer customers.
DiceFiltering data by multiple dimensions. Example: Sales for 2025 in the West region.
DimensionA descriptive attribute used to slice data. Example: Date, region, product.
Dimension TableA table containing descriptive attributes. Example: Product details.
DimensionalityNumber of features or variables in data. Example: High-dimensional customer data.
DistributionHow values are spread across a range. Example: Income distribution.
Drill DownNavigating from summary to detail. Example: Yearly sales → monthly sales.
Drill ThroughJumping to a detailed view for a specific value. Example: Clicking a region to see store data.
ELTExtract, Load, Transform approach. Example: Transforming data inside a warehouse.
ETLExtract, Transform, Load process. Example: Loading CRM data into a warehouse.
Exploratory Data Analysis (EDA)Initial investigation to understand data. Example: Visualizing distributions.
Fact TableA table containing quantitative data. Example: Sales transactions.
FeatureAn individual measurable property used in analysis. Example: Customer age used in churn analysis.
Feature EngineeringCreating new features from existing data. Example: Calculating customer tenure from signup date.
FilteringLimiting data to a subset of interest. Example: Only orders from 2025.
GranularityThe level of detail in the data. Example: Daily sales vs. monthly sales.
GroupingOrganizing data into categories before aggregation. Example: Sales grouped by product category.
HistogramA chart showing data distribution. Example: Frequency of order sizes.
HypothesisA testable assumption. Example: Discounts increase sales.
Incremental LoadLoading only new or changed data. Example: Yesterday’s transactions.
InsightA meaningful finding that informs action. Example: High churn among new users.
KPI (Key Performance Indicator)A critical metric tied to business objectives. Example: Monthly churn rate.
KurtosisMeasure of how heavy the tails of a distribution are. Example: Detecting extreme outliers.
LatencyDelay between data generation and availability. Example: Real-time vs. daily data.
Line ChartA chart showing trends over time. Example: Monthly revenue trend.
MeanThe arithmetic average. Example: Average order value.
MeasureA calculated numeric value, often aggregated. Example: SUM(Sales).
MedianThe middle value in ordered data. Example: Median household income.
MetricA quantifiable measure used to track performance. Example: Total sales, average order value.
Missing ValuesData points that are absent or null. Example: Blank customer age values.
ModeThe most frequent value. Example: Most common product category.
Multivariate AnalysisAnalyzing multiple variables simultaneously. Example: Studying price, demand, and seasonality.
NormalizationScaling data to a common range. Example: Normalizing values between 0 and 1.
ObservationA single record or row in a dataset. Example: One customer’s purchase history.
OutlierA data point significantly different from others. Example: An unusually large transaction amount.
PercentileValue below which a percentage of data falls. Example: 90th percentile response time.
PopulationThe full set of interest. Example: All customers.
Predictive AnalyticsAnalysis that forecasts future outcomes. Example: Predicting next month’s demand.
Prescriptive AnalyticsAnalysis that suggests actions. Example: Recommending price changes.
QuartileValues dividing data into four parts. Example: Q1, Q2, Q3.
ReportA structured presentation of analysis results. Example: Monthly performance report.
ReproducibilityAbility to recreate analysis results consistently. Example: Using versioned datasets.
Rolling AverageAn average calculated over a moving window. Example: 7-day rolling average of sales.
Root Cause AnalysisIdentifying the underlying cause of an issue. Example: Revenue loss due to inventory shortages.
SampleA subset of a population. Example: Survey respondents.
Sampling BiasBias introduced by non-random samples. Example: Feedback collected only from power users.
Scatter PlotA chart showing relationships between two variables. Example: Ad spend vs. revenue.
SeasonalityRepeating patterns tied to time cycles. Example: Holiday sales spikes.
Semi-Structured DataData with flexible structure. Example: JSON files.
Sensitivity AnalysisEvaluating how outcomes change with inputs. Example: Impact of price changes on profit.
SliceFiltering data by a single dimension. Example: Sales for 2025 only.
SnapshotData captured at a specific point in time. Example: End-of-month balances.
Snowflake SchemaA normalized version of a star schema. Example: Product broken into sub-tables.
Standard DeviationAverage distance from the mean. Example: Consistency of sales performance.
StandardizationRescaling data to have mean 0 and standard deviation 1. Example: Preparing data for regression analysis.
Star SchemaA data model with facts surrounded by dimensions. Example: Sales fact with product and date dimensions.
Structured DataData with a fixed schema. Example: Relational tables.
Time SeriesData indexed by time. Example: Daily stock prices.
TrendA general direction in data over time. Example: Increasing monthly revenue.
Unstructured DataData without a predefined schema. Example: Emails, images.
VariableA characteristic or attribute that can take different values. Example: Age, revenue, product category.
VarianceMeasure of data spread. Example: Variance in delivery times.

Please share your suggestions for any terms that should be added.