DP-203 Secure, monitor, and optimize data storage and data processing Practice Test 8 — 15 Questions

Question 1

Your team has deployed an Azure Stream Analytics job that writes output to Azure Cosmos DB. You need to monitor the job for data latency and ensure it meets a service-level agreement (SLA) of under 10 seconds from input to output. Which metric should you track in Azure Monitor?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Watermark delay.. Watermark delay is the correct metric to monitor for data latency because it measures the maximum time between an input event being received and the corresponding output being produced. A watermark delay consistently under 10 seconds ensures the SLA is met. Output events (A) track the number of output events, not latency. Runtime errors (B) indicate failures, not latency. Input events (D) track the number of input events, not latency.

Answer

Output events.

Answer

Runtime errors.

Answer

Input events.

Question 2

You are optimizing an Azure Synapse Analytics dedicated SQL pool that is experiencing high concurrency and frequent resource class contention. You need to improve query performance and reduce contention without changing the workload. Which two actions should you take? (Choose two.)

Accepted Answer

Implement workload isolation to separate critical queries into dedicated resource groups.. Options B and D are correct. Workload isolation and workload classification help manage resource allocation and reduce contention. Option A is wrong because increasing DWU may help but does not address contention directly. Option C is wrong because materialized views improve performance but do not reduce contention. Option E is wrong because result set caching helps read workloads but not contention.

Answer

Increase the DWU (Data Warehouse Units) to allocate more resources.

Answer

Create materialized views to pre-aggregate data.

Answer

Enable result set caching to reduce repeated query execution.

Question 3

Your organization uses Azure Data Lake Storage Gen2 and needs to prevent accidental deletion of data by enabling soft delete. You also need to ensure that deleted blobs are recoverable for 30 days. What should you configure?

Accepted Answer

Enable blob soft delete and set retention period to 30 days.. Blob soft delete enables recovery of deleted blobs within a specified retention period (30 days in this case). Option A is incorrect because blob snapshots are point-in-time copies that require manual management and do not provide automatic recovery of deleted blobs. Option B is incorrect because Azure Backup is designed for virtual machines and other Azure resources, not for blob-level recovery in Data Lake Storage Gen2. Option C is incorrect because container soft delete deletes entire containers, not individual blobs.

Answer

Enable blob snapshots and set them to expire after 30 days.

Answer

Use Azure Backup to create daily backups of the storage account.

Answer

Enable container soft delete with a retention period of 30 days.

Question 4

You are designing a security strategy for Azure Synapse Analytics. The solution must prevent users from accessing sensitive columns in a dedicated SQL pool, such as Social Security numbers, unless they have explicit permission. Which feature should you use?

Accepted Answer

Column-level security.. Column-level security (CLS) restricts access to specific columns by granting or denying SELECT permissions on individual columns. Option C (Dynamic data masking) obfuscates data at query time but does not prevent access, as users can still see the data if they bypass masking. Option B (Azure Purview) is a data governance service for cataloging and classifying data, not for access control. Option D (Row-level security) filters rows based on user context, not columns.

Answer

Azure Purview data classification.

Answer

Dynamic data masking.

Answer

Row-level security (RLS).

Question 5

Your company uses Azure Data Factory to orchestrate data movement. You need to monitor pipeline runs across multiple factories and create a dashboard that shows success and failure rates over the past 30 days. What is the most efficient approach?

Accepted Answer

Configure diagnostic settings for each Data Factory to send logs to a Log Analytics workspace, then create a workbook using KQL queries.. Configuring diagnostic settings for each Data Factory to send logs to a Log Analytics workspace enables querying and visualizing pipeline runs across multiple factories in a single dashboard using Azure Monitor workbooks with KQL queries. Option A is inefficient as it requires viewing each factory individually. Option B is incorrect because Azure Storage Analytics logs storage metrics, not Data Factory pipeline runs. Option D is inefficient because manual aggregation is not scalable.

Answer

Use the Data Factory monitoring UI to view runs for each factory individually.

Answer

Enable Azure Storage Analytics and query the logs stored in a storage account.

Answer

Create alert rules in Azure Monitor for each pipeline failure and aggregate manually.

Question 6

You are optimizing an Azure Synapse Analytics dedicated SQL pool that runs a mix of reporting and ETL workloads. The ETL jobs often encounter resource wait times due to concurrent reporting queries. You need to ensure that ETL jobs always get the resources they need. Which two actions should you take? (Choose two.)

Accepted Answer

Assign HIGH importance to the ETL workload classifier.. The correct actions are B and E. Assigning HIGH importance to the ETL workload classifier (B) ensures that ETL queries are prioritized over lower-importance reporting queries. Creating a workload group for ETL with a minimum resource percentage and assigning it to a dedicated resource pool (E) guarantees a baseline of resources for ETL, preventing resource starvation. Option A (increasing DWU) adds overall resources but does not guarantee ETL gets priority. Option C (result set caching) and Option D (materialized views) improve reporting performance but do not ensure ETL resource allocation.

Answer

Increase the DWU (Data Warehouse Units) to provide more overall resources.

Answer

Enable result set caching for reporting queries.

Answer

Create materialized views for common reporting aggregations.

Question 7

You are using Azure Data Factory to ingest data from a REST API into Azure Synapse Analytics. The API has a rate limit of 100 requests per minute. You need to ensure that the pipeline respects the rate limit and retries on failure. Which two settings should you configure in the copy activity? (Choose two.)

Accepted Answer

Set the 'Throttle' property to limit the number of concurrent connections.. Options C and D are correct. Setting the 'Throttle' property (or 'maxConcurrentConnections') limits the number of concurrent connections to the REST API, which helps respect the rate limit of 100 requests per minute. Setting the 'Retry' property to a value greater than 0 enables the activity to retry on transient failures. Option A is wrong because 'Enable staging' is used for large data transfers or to enable PolyBase, not for rate limiting. Option B is wrong because 'Batch size' controls the number of rows per batch for bulk operations, not the request rate. Option E is wrong because 'Timeout' specifies the maximum duration of the activity, not retry behavior.

Answer

Enable 'Enable staging' to use a staging blob.

Answer

Configure the 'Batch size' to 100.

Answer

Increase the 'Timeout' value to 10 minutes.

Question 8

You need to monitor the performance of an Azure Stream Analytics job that processes real-time IoT data. Which metric indicates the number of events that are being dropped or delayed due to insufficient processing capacity?

Accepted Answer

Backlogged input events.. Backlogged input events (C) measures the number of events that are queued awaiting processing, indicating that the job is unable to keep up with the input rate. High backlog suggests insufficient processing capacity, leading to dropped or delayed events. Watermark delay (A) measures the time lag in processing, but does not directly count events dropped. Input events (D) is the total received, not dropped/delayed. Output events (B) is the total sent.

Answer

Watermark delay.

Answer

Output events.

Answer

Input events.

Question 9

Your company uses Azure Data Lake Storage Gen2 and needs to implement a data retention policy that automatically deletes files older than 90 days in a specific container. What should you use?

Accepted Answer

Azure Storage lifecycle management rule with a filter for the container and a delete action after 90 days.. Azure Storage lifecycle management rules can be applied at the container level to automatically delete blobs after a specified age (90 days), providing a native, cost-effective, and automated retention solution. Option A is incorrect because Azure Data Factory can be scheduled to delete files, but it requires manual pipeline setup and incurs additional costs compared to lifecycle management. Option B is incorrect because Azure Policy with a deny effect prevents the creation or modification of resources, not the automatic deletion of existing files. Option D is incorrect because Azure Purview is a data governance service for cataloging and lineage, not for automated data deletion.

Answer

Azure Data Factory pipeline with a Delete activity scheduled to run daily.

Answer

Azure Policy with a deny effect for files older than 90 days.

Answer

Azure Purview data lifecycle policy.

Question 10

You are designing a disaster recovery plan for an Azure Synapse Analytics dedicated SQL pool. The primary region becomes unavailable. You need to fail over to a secondary region with minimal data loss. The recovery point objective (RPO) is 1 hour. What should you configure?

Accepted Answer

Enable geo-backup on the dedicated SQL pool.. Geo-backup is a built-in feature for Azure Synapse Analytics dedicated SQL pools that automatically takes full backups at regular intervals and replicates them to a paired region. The default recovery point objective (RPO) is 1 hour, meeting the requirement. Option A is incorrect because active geo-replication is a feature for Azure SQL Database, not for Synapse dedicated SQL pools. Option B is incorrect because automatic restore points are local and not automatically replicated; copying them via Azure Data Factory adds complexity and does not guarantee the RPO. Option D is incorrect because user-defined restore points are manual and would require additional orchestration to store them in a secondary region, making it less reliable and harder to meet the 1-hour RPO consistently.

Answer

Configure active geo-replication to the secondary region.

Answer

Use automatic restore points and copy them to the secondary region using Azure Data Factory.

Answer

Create user-defined restore points every hour and store them in the secondary region.

Question 11

You need to ensure that an Azure Data Factory pipeline can copy data from an Azure SQL Database that is behind a private endpoint. The Data Factory should use a managed virtual network. What should you configure?

Accepted Answer

Enable managed virtual network for the Data Factory and create a managed private endpoint for the SQL Database.. A managed private endpoint in the Data Factory's managed virtual network allows secure access to the SQL Database's private endpoint. Option A is wrong because the integration runtime must be in the same virtual network. Option B is wrong because self-hosted IR is for on-premises data sources. Option D is wrong because Azure Bastion is for VM access, not data factory.

Answer

Install a self-hosted integration runtime on a VM in the same virtual network.

Answer

Use the default Azure integration runtime.

Answer

Use Azure Bastion to connect the Data Factory to the SQL Database.

Question 12

You have an Azure Synapse Analytics dedicated SQL pool that stores sensitive customer data. You need to ensure that only users with a specific Microsoft Entra ID role can access the data, and all access must be logged for auditing. What should you implement?

Accepted Answer

Row-level security (RLS) with a security policy. Row-level security (RLS) is the correct choice because it restricts data access at the row level based on user context, such as Microsoft Entra ID role membership, and all access can be logged via auditing. Dynamic data masking (A) obfuscates data but does not restrict access. Azure RBAC (B) controls management plane access, not data access within the SQL pool. Column-level security (D) restricts access to specific columns, not rows.

Answer

Dynamic data masking

Answer

Azure RBAC at the SQL pool level

Answer

Column-level security

Question 13

Your company uses Azure Data Lake Storage Gen2 with hierarchical namespace enabled. You need to optimize costs for a large dataset that is accessed only once a month for reporting. The data must be retained for 7 years. Which storage tier and lifecycle management rule should you configure?

Accepted Answer

Cool tier with lifecycle policy to Archive after 30 days. Cool tier is cost-effective for data accessed infrequently (once a month), and a lifecycle policy to move data to Archive after 30 days further reduces costs while meeting the 7-year retention requirement. Option A (Hot tier) is expensive for infrequent access. Option C (Premium tier) is designed for high-throughput workloads, not cost optimization. Option D (Archive tier with no lifecycle) incurs high retrieval costs for monthly access and doesn't leverage tiering.

Answer

Hot tier with no lifecycle policy

Answer

Premium tier with lifecycle policy to Cool after 30 days

Answer

Archive tier with no lifecycle policy

Question 14

You are monitoring an Azure Data Factory pipeline that runs hourly. You notice that the pipeline occasionally fails due to transient errors. Which monitoring solution should you use to get alerts on failures and analyze trends over time?

Accepted Answer

Azure Monitor with Log Analytics workspace. Azure Monitor with alerts and Log Analytics provides historical analysis and alerting. Option A (Data Factory Monitor) is for real-time monitoring but lacks long-term trend analysis. Option C (Azure Dashboard) is a visualization tool. Option D (Event Grid) is for event-driven notifications, not analysis.

Answer

Azure Event Grid subscription for pipeline failures

Answer

Azure Dashboard pinned with pipeline metrics

Answer

Azure Data Factory Monitor in the Azure portal

Question 15

You are an Azure administrator. You apply the Azure Policy shown in the exhibit to a management group. What is the outcome of this policy?

Accepted Answer

It denies storage accounts that allow public network access.. The policy denies the creation of storage accounts that allow public network access. Therefore, Option B is correct. Option A is incorrect because the policy does not require a firewall rule; it denies public access entirely. Option C is incorrect because the policy does not address HTTPS enforcement. Option D is incorrect because the policy only denies storage accounts that allow public network access, not all storage accounts.

Answer

It allows storage accounts only if they have a firewall rule.

Answer

It requires all storage accounts to use HTTPS only.

Answer

It denies the creation of any new storage account.