CCNA Deploy Maintain Questions

MCQhard

You are reviewing the deployment configuration for a Power BI dataset. The exhibit shows a JSON snippet of the dataset settings. You need to ensure that data is refreshed twice a day at 6:00 AM and 6:00 PM UTC. However, the refresh fails at both scheduled times. What is the most likely cause?

A.The data source uses Integrated Security (SSPI) which is not supported for scheduled refresh.

B.The refresh schedule uses UTC but the data source is in a different time zone.

C.The dataset has DirectQuery enabled, which prevents Import mode refresh.

D.The gateway ID is incorrect.

AnswerA

Integrated Security (SSPI) depends on the interactive user's Windows token to authenticate to the data source. In the Power BI service, scheduled refresh runs under a non-interactive service account, so there is no Windows identity to pass through. The service requires explicit stored credentials (e.g., SQL Server authentication or OAuth) for scheduled refresh, not SSPI. Because the data source is configured for SSPI, the refresh fails at the credential validation step.

Why this answer

Scheduled refresh in Power BI requires a gateway to connect to on-premises data sources, and when the data source uses Integrated Security (SSPI), the gateway cannot delegate credentials for scheduled refresh. SSPI relies on the user's interactive Windows authentication context, which is not available during unattended scheduled refresh operations. This causes the refresh to fail at both scheduled times.

Exam trap

The trap here is that candidates often assume time zone mismatches or DirectQuery settings cause refresh failures, but the real issue is that Integrated Security (SSPI) requires interactive user context and is not supported for unattended scheduled refresh without proper delegation configuration.

How to eliminate wrong answers

Option B is wrong because time zone differences do not cause refresh failures; the refresh schedule simply runs at the specified UTC times regardless of the data source's time zone. Option C is wrong because DirectQuery does not prevent Import mode refresh; a dataset can have both DirectQuery and Import partitions, and scheduled refresh applies only to Import mode tables. Option D is wrong because an incorrect gateway ID would cause a connection error at the time of refresh, but the question states the refresh fails at both scheduled times, which is consistent with a credential delegation issue rather than a gateway misconfiguration.

Practice this question →

MCQeasy

You have developed a Power BI report that uses a live connection to an Azure Analysis Services (AAS) model. The AAS model is deployed in a different Azure region. Users report that the report loads slowly, sometimes taking over 30 seconds to render a single visual. You need to improve performance without changing the data model or the report structure. What should you do?

A.Enable the 'Reduce data shown in visuals' option in Power BI Desktop.

B.Implement row-level security (RLS) in the Azure Analysis Services model.

C.Deploy the Azure Analysis Services model to the same region as the Power BI workspace.

D.Convert the report to import mode and schedule a refresh.

AnswerC

A live connection means each Power BI visual executes a DAX query through the network to the Analysis Services instance, so physical distance directly affects response time. By co-locating the AAS server and the Power BI workspace in the same Azure region, round-trip network latency is minimized and data can stay within a single data center, which yields the fastest live query performance. This is a low-effort architectural fix that reduces latency without adding any per-query overhead or altering data granularity.

Why this answer

Deploying the Azure Analysis Services model to the same Azure region as the Power BI workspace minimizes network latency between the live connection client (Power BI) and the tabular model server. A live connection sends DAX queries over the XMLA protocol, and cross-region traffic introduces significant round-trip delays, which directly causes slow visual rendering. By co-locating the AAS server and the Power BI service in the same region, you reduce the physical distance and network hops, improving query response time without altering the model or report.

Exam trap

The trap here is that candidates often confuse client-side optimization settings (like reducing data shown) with server-side performance fixes, or they incorrectly assume that adding RLS or switching to import mode are acceptable solutions when the question explicitly prohibits changing the data model or report structure.

How to eliminate wrong answers

Option A is wrong because 'Reduce data shown in visuals' is a Power BI Desktop setting that limits the number of data points displayed in a visual (e.g., top N), which does not reduce the underlying query workload or network latency for a live connection to AAS; it only affects client-side rendering. Option B is wrong because implementing row-level security (RLS) in the AAS model adds additional DAX evaluation overhead for each query, which would likely worsen performance rather than improve it, and it does not address the cross-region latency issue. Option D is wrong because converting the report to import mode would require changing the data model (from live to import) and would break the live connection requirement; it also introduces a scheduled refresh dependency, which contradicts the constraint of not changing the data model or report structure.

Practice this question →

Multi-Selectmedium

Which TWO actions are required to set up a Power BI deployment pipeline with separate data sources for Development and Production?

Select 2 answers

A.Define dataset parameters in Power BI Desktop for data source values.

B.Assign the dataset owner to each stage workspace.

C.Configure parameter rules in the deployment pipeline to map parameters to stage-specific values.

D.Re-enter data source credentials for each stage after deployment.

E.Create separate gateways for each stage.

AnswersA, C

Parameters allow you to change data source at deployment time.

Why this answer

Defining dataset parameters in Power BI Desktop for data source values allows you to make the data source connection dynamic. These parameters can then be overridden at each stage of the deployment pipeline, enabling separate data sources for Development and Production without modifying the report file.

Exam trap

The trap here is that candidates often confuse credential management (Option D) with parameter-based data source separation, or assume that separate gateways (Option E) are mandatory, when in fact parameter rules are the correct and efficient method for stage-specific data sources.

Practice this question →

MCQmedium

You publish a Power BI report to a workspace that uses an organizational app. After updating the report, you want users to see the changes immediately without having to reinstall the app. What should you do?

A.Delete and recreate the app from the workspace.

B.Republish the report from Power BI Desktop.

C.Ask users to refresh their browser cache.

D.Update the app in the workspace by selecting 'Update app'.

AnswerD

In the Power BI service, the correct way to propagate workspace changes to the audience is to open the workspace and click "Update app" (or "Update app" in the app editing screen). This action re-publishes the app's content, including the modified report, to all current app users without interrupting their access or requiring them to reinstall the app. It also ensures the app's metadata, navigation, and permissions remain intact. This is the standard lifecycle step after editing a report or dashboard in the source workspace.

Why this answer

Updating the app in the workspace by selecting 'Update app' publishes the latest version of the report (and any other content) to the existing app without requiring users to reinstall. The app is a container that points to the workspace content; updating it refreshes that pointer, making changes immediately available to users who already have the app installed.

Exam trap

The trap here is that candidates confuse updating the workspace content (e.g., republishing a report) with updating the app itself, assuming changes automatically propagate to the app without an explicit 'Update app' step.

How to eliminate wrong answers

Option A is wrong because deleting and recreating the app forces users to reinstall the app from AppSource, causing unnecessary disruption and potential loss of app permissions or custom settings. Option B is wrong because republishing the report from Power BI Desktop only updates the report in the workspace, not the app itself; users would still see the old version until the app is updated. Option C is wrong because refreshing the browser cache does not affect the app's published content; the app is a separate deployment artifact that must be explicitly updated to reflect workspace changes.

Practice this question →

MCQeasy

You need to deploy a Power BI report from a development workspace to a production workspace. You want to ensure that the report uses the production dataset connection string without manual changes. What should you use?

A.Manually update the data source in the production workspace after deployment.

B.Use a Power BI template (.pbit) and change the connection string before publishing.

C.Configure a deployment pipeline with parameter rules to override the data source.

D.Publish the report directly to the production workspace and update the dataset.

AnswerC

Power BI Deployment Pipelines allow you to assign parameter rules (e.g., for data source parameters like Server and Database) that override values automatically when content is deployed from dev to test to production. This is the correct choice because it is the only option that truly meets the requirement of the report automatically using the production connection string without any manual adjustment. The rules are configured once in the pipeline and then applied consistently on every deployment, making the process repeatable and governed.

Why this answer

Deployment pipelines in Power BI allow you to define parameter rules that automatically override data source connection strings when content is deployed from development to production. This ensures the report uses the production dataset without any manual post-deployment changes, maintaining consistency and reducing human error.

Exam trap

The trap here is that candidates often confuse manual post-deployment updates (options A and D) or template-based approaches (option B) with the automated, rule-based override mechanism provided by deployment pipelines, which is the only method that meets the 'no manual changes' requirement.

How to eliminate wrong answers

Option A is wrong because manually updating the data source after deployment contradicts the requirement of avoiding manual changes and introduces risk of errors. Option B is wrong because using a .pbit template still requires manual intervention to change the connection string before publishing, which does not automate the process. Option D is wrong because publishing directly to production and then updating the dataset requires manual steps and does not leverage automated deployment rules.

Practice this question →

MCQmedium

You are the Power BI administrator for a large enterprise. The company has a Power BI Premium capacity with a single dataset that is used by multiple reports and dashboards. The dataset is refreshed daily at 3:00 AM, and the refresh typically completes within 2 hours. Recently, users have reported that the dataset is not showing the most recent data until after 6:00 AM. You investigate and find that the scheduled refresh is taking 4 hours to complete, and there are no errors in the refresh history. The dataset uses import mode and connects to an on-premises SQL Server data warehouse. The data model contains several large fact tables and multiple calculated tables and measures. What should you do to reduce the refresh time and ensure data is available by 5:00 AM?

A.Remove all calculated tables and measures and replace them with calculated columns in Power Query

B.Implement incremental refresh on the fact tables to refresh only new and changed data

C.Change the dataset storage mode to DirectQuery to avoid the import process

D.Install an additional on-premises data gateway and configure load balancing

AnswerB

Implementing incremental refresh on fact tables is the correct solution because it partitions the table by date and only processes partitions that are new or changed since the last refresh, dramatically reducing the amount of data pulled from the source and the storage engine workload. This requires an import-mode dataset with a date-time watermark column, RangeStart and RangeEnd parameters, and proper policy settings for archive periods; it directly targets the root cause of a prolonged refresh cycle by limiting the refresh scope to deltas instead of reprocessing the entire fact table history.

Why this answer

Implementing incremental refresh on the fact tables allows Power BI to refresh only new or changed data instead of the entire dataset each time. This significantly reduces the refresh window, especially for large fact tables, because only the latest partition (e.g., today's data) is processed. Since the scheduled refresh starts at 3:00 AM and must complete by 5:00 AM, incremental refresh can cut the refresh time from 4 hours to under 2 hours by avoiding reprocessing historical data.

Exam trap

The trap here is that candidates often choose Option C (DirectQuery) thinking it eliminates refresh time entirely, but they overlook that DirectQuery changes the entire query model and is not a direct fix for a scheduled import refresh that is simply taking too long due to data volume.

How to eliminate wrong answers

Option A is wrong because replacing calculated tables and measures with calculated columns in Power Query does not reduce refresh time; calculated columns are computed during data load and can actually increase memory and processing overhead, while measures are computed at query time and have no impact on refresh duration. Option C is wrong because changing the dataset storage mode to DirectQuery would bypass the import process entirely, but it would also eliminate the benefits of import mode (such as fast query performance) and would require the on-premises SQL Server to handle all query loads, potentially causing performance issues and breaking existing reports that rely on import-mode features like calculated tables. Option D is wrong because installing an additional on-premises data gateway and configuring load balancing improves gateway throughput and reliability but does not address the root cause of slow refresh—the full reload of large fact tables; the gateway is not the bottleneck here since there are no errors in refresh history and the issue is the volume of data being refreshed.

Practice this question →

MCQmedium

You manage a Power BI workspace used by the sales team. After updating a dataset with new columns, some users report that their reports show old data. You verify that the scheduled refresh completed successfully. What should you do first?

A.Open the report in Power BI Desktop, refresh the dataset, and republish.

B.Clear the users' browser cache.

C.Reconfigure the scheduled refresh to run more frequently.

D.Reset the on-premises data gateway.

AnswerA

Opening the report in Power BI Desktop and refreshing the dataset forces Power Query to re-evaluate the data source schema, including renamed, removed, or added columns. After the refresh, republishing the .pbix file overwrites the dataset in the Power BI service, replacing the stale metadata and ensuring the report's visuals and calculations bind to the current schema. This is the only option that directly updates the dataset definition, not just the data values.

Why this answer

When a dataset is updated with new columns in Power BI Desktop, the report's underlying data model must be refreshed and republished to the Power BI service. Even if the scheduled refresh completes successfully, it only refreshes the existing data structure; it does not automatically incorporate schema changes like new columns. Republishing the .pbix file ensures the service has the updated metadata and data.

Exam trap

The trap here is that candidates assume a successful scheduled refresh automatically propagates all changes, including schema modifications, when in fact it only refreshes data within the existing model structure.

How to eliminate wrong answers

Option B is wrong because clearing the users' browser cache would not resolve the issue of missing new columns; the report in the service still has the old schema. Option C is wrong because increasing the scheduled refresh frequency does not add new columns to the dataset; it only refreshes existing data. Option D is wrong because resetting the on-premises data gateway is unrelated to schema changes; the gateway handles data movement, not dataset structure updates.

Practice this question →

MCQeasy

You need to ensure that a Power BI report uses the latest data from a cloud-based Azure SQL Database. The report is configured with scheduled refresh. What is the minimum required license for the dataset owner to configure a scheduled refresh?

A.Power BI Premium capacity

B.Power BI Premium Per User

C.Power BI Free

D.Power BI Pro

AnswerD

Power BI Pro is the correct minimum license because scheduled refresh in the Power BI service is a Pro-only feature. With a Pro license, you can create datasets, publish them to shared capacity, and configure a refresh schedule without needing Premium capacity or Premium Per User. A Pro license is sufficient for the standard, recurring data refresh scenario in shared capacity.

Why this answer

Power BI Pro is the minimum license required for a dataset owner to configure a scheduled refresh against a cloud-based Azure SQL Database. Scheduled refresh is a premium feature that is not available with a Power BI Free license, and while Power BI Premium capacity or Premium Per User can also support scheduled refresh, they are not the minimum requirement. The dataset owner must have a Pro license to schedule refreshes for datasets hosted in shared capacity.

Exam trap

The trap here is that candidates often assume that because Azure SQL Database is a cloud source, a Free license might suffice, but Power BI Free does not support any form of scheduled refresh, regardless of the data source being cloud or on-premises.

How to eliminate wrong answers

Option A is wrong because Power BI Premium capacity is an organizational-level licensing option that provides dedicated capacity and additional features, but it is not the minimum license required for an individual dataset owner to configure scheduled refresh. Option B is wrong because Power BI Premium Per User is a per-user license that grants premium features, but it is not the minimum requirement; a Pro license suffices for scheduled refresh in shared capacity. Option C is wrong because Power BI Free license does not allow scheduled refresh; it only permits manual refresh via Power BI Desktop or the service, and the dataset owner cannot configure automatic scheduled refresh without a Pro or higher license.

Practice this question →

MCQhard

You are a Power BI administrator for a large organization. A team has published a shared dataset to a Premium workspace. They use an XMLA endpoint to programmatically refresh the dataset daily. Recently, the refresh started failing with the error: 'The operation was canceled because the session was terminated by a concurrent operation.' The dataset is not partitioned. You need to ensure the refresh completes without errors. What should you do?

A.Remove all scheduled refreshes and rely solely on the XMLA script.

B.Enable the 'Refresh conflict detection' setting on the dataset to prevent concurrent operations.

C.Change the XMLA script to run less frequently, e.g., once per week.

D.Partition the dataset and refresh partitions sequentially.

AnswerB

Enabling the 'Refresh conflict detection' setting on the dataset is the native solution for this scenario. In Power BI Premium, this setting detects when a second refresh request arrives while a refresh is already executing, and it blocks the conflicting request instead of allowing them to overlap. This eliminates the 'refresh already in progress' error while keeping both the scheduled and XMLA refresh workflows intact.

Why this answer

The error 'The operation was canceled because the session was terminated by a concurrent operation' indicates that two refresh operations are conflicting on the same dataset. Enabling 'Refresh conflict detection' on the dataset prevents concurrent refreshes by queuing or blocking overlapping operations, ensuring the XMLA-triggered refresh completes without interruption.

Exam trap

The trap here is that candidates often assume the error is caused by the XMLA script itself (e.g., frequency or scheduling) and overlook the fact that the conflict is due to concurrent operations, which is directly solved by enabling conflict detection rather than changing the script's schedule or partitioning strategy.

How to eliminate wrong answers

Option A is wrong because removing scheduled refreshes does not address conflicts that could arise from other concurrent operations, such as multiple XMLA scripts or user interactions; the error specifically points to a concurrent session, not just scheduled refreshes. Option C is wrong because reducing the frequency of the XMLA script does not prevent conflicts when the script runs; if another operation occurs at the same time, the conflict persists regardless of frequency. Option D is wrong because partitioning the dataset and refreshing partitions sequentially does not resolve the fundamental issue of concurrent sessions; the error is about session termination, not partition-level parallelism, and sequential partition refreshes can still conflict if multiple sessions attempt to refresh the same dataset.

Practice this question →

Drag & Dropmedium

Drag and drop the steps to create a relationship between two tables in Power BI Desktop into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

Why this order

Relationships are created by dragging columns between tables in the Model view, then confirming settings like cardinality.

Practice this question →

MCQeasy

A user reports that a Power BI report is not refreshing data from a SQL Server database. The dataset uses Import mode. The gateway cluster shows all gateways are online. What is the most likely cause?

A.The report is using a scheduled refresh with a conflicting time.

B.The gateway version is incompatible with the SQL Server version.

C.The dataset uses DirectQuery mode which requires a live connection.

D.The data source credentials are incorrect or expired.

AnswerD

Incorrect or expired data source credentials are the most frequent cause of a Power BI scheduled refresh failure. When you configure a dataset for refresh, Power BI stores the authentication values—Windows credentials, database passwords, or OAuth tokens—and if those are changed or time out, the service cannot authenticate to the source and the refresh operation fails with an error. Updating the credentials in the dataset settings under 'Edit credentials' resolves the issue, and you should verify that the account still has the same permissions.

Why this answer

In Import mode, Power BI caches data and refreshes it on a schedule using stored credentials. If those credentials expire or become invalid, the refresh fails even though the gateway cluster shows as online. The gateway being online only indicates network connectivity, not that the stored credentials are still valid for the SQL Server database.

Exam trap

The trap here is that candidates see 'gateway cluster shows all gateways are online' and assume the issue must be elsewhere, but gateway online status does not validate the stored data source credentials, which are a separate authentication layer that can expire or become invalid independently.

How to eliminate wrong answers

Option A is wrong because a conflicting scheduled refresh time would cause a refresh to be skipped or queued, not a persistent failure; the report would still refresh at the next available window. Option B is wrong because gateway version incompatibility with SQL Server version is extremely rare; the gateway communicates via standard TDS protocol and is backward-compatible with most SQL Server versions. Option C is wrong because the question explicitly states the dataset uses Import mode, not DirectQuery mode, so the requirement for a live connection is irrelevant.

Practice this question →

MCQmedium

You are deploying a Power BI solution to a customer. The customer requires that all report access be controlled via Azure Active Directory (Azure AD) groups. You have a single workspace with multiple reports. What is the best practice for managing permissions?

A.Create a Power BI group and add users to it.

B.Assign each user directly to the workspace role.

C.Share each report individually with users.

D.Add an Azure AD group to the workspace role.

AnswerD

Adding an Azure AD group to the workspace role is the correct, recommended approach for scalable access management in Power BI. When you assign the group to a role such as Viewer, Contributor, Member, or Admin, all current and future members of that group automatically receive the corresponding permissions on the workspace and its content. This centralizes identity governance in Azure AD: adding or removing a user from the group instantly reflects in Power BI, with no per-workspace or per-report edits needed. It aligns with enterprise security best practices and simplifies auditing and compliance.

Why this answer

Using an Azure AD group to manage workspace roles aligns with the customer's requirement for centralized access control via Azure AD. This approach simplifies permission management by allowing group membership changes in Azure AD to automatically propagate to Power BI workspace access, ensuring consistency and reducing administrative overhead.

Exam trap

The trap here is that candidates may confuse Power BI groups (which are legacy and not Azure AD integrated) with Azure AD groups, or assume that direct user assignment or individual report sharing is simpler, missing the requirement for centralized Azure AD-based control.

How to eliminate wrong answers

Option A is wrong because creating a Power BI group (a distribution group or security group within Power BI) does not leverage Azure AD groups as required; it introduces a separate group management layer that is not integrated with the customer's Azure AD-based identity governance. Option B is wrong because assigning each user directly to the workspace role violates the requirement to control access via Azure AD groups, leading to manual, error-prone user management and lack of centralized control. Option C is wrong because sharing each report individually bypasses workspace-level permissions, creating a fragmented permission model that is harder to audit and does not scale; it also does not use Azure AD groups as specified.

Practice this question →

MCQmedium

You manage a Power BI workspace that contains a dataset refreshed daily from an on-premises SQL Server. Users report that the report shows data from two days ago. You verify that the scheduled refresh ran successfully this morning. What is the most likely cause?

A.The on-premises data source is misconfigured, causing the refresh to load data from an outdated source.

B.The refresh took longer than expected and timed out.

C.The scheduled refresh is not set to refresh the dataset.

D.The on-premises data gateway is offline.

AnswerA

Correct. A misconfigured on-premises data source in the Power BI service — such as a gateway data source entry pointing to the wrong server, database, or folder, or using credentials for a different environment — can cause the refresh engine to successfully connect to and load data from an outdated or unintended location. Because the refresh completes without error, the service reports success even though the dataset is populated from the wrong source. This perfectly matches the scenario: a successful refresh that yields stale data.

Why this answer

The most likely cause is that the on-premises data source is misconfigured (e.g., pointing to a stale backup or snapshot). This results in the scheduled refresh loading data from an outdated source, making it appear successful yet yielding old data. The gateway does not cache data; the issue lies in the data source reference.

Exam trap

Candidates may assume a successful refresh guarantees current data, but misconfiguration of the data source (e.g., wrong connection string pointing to a backup) can cause the refresh to load outdated data without failure.

How to eliminate wrong answers

Option B is wrong because if the refresh took longer than expected and timed out, the refresh would not have completed successfully, but the question states the scheduled refresh ran successfully. Option C is wrong because the scheduled refresh is explicitly set to refresh the dataset daily, and the question confirms it ran successfully this morning. Option D is wrong because if the on-premises data gateway were offline, the scheduled refresh would fail entirely, not run successfully and still show stale data.

Practice this question →