DP-203 Secure, monitor, and optimize data storage and data processing Practice Test 5 — 15 Questions

Question 1

You are using Azure Stream Analytics to process real-time data from an event hub and output to Azure Synapse Analytics. You need to ensure exactly-once delivery semantics to the output. What should you configure?

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

Accepted Answer

Set the output to 'Exactly Once' delivery mode.. Azure Stream Analytics supports exactly-once delivery to Azure Synapse Analytics by configuring the output to 'Exactly Once' delivery mode. Option A is incorrect because the input consumption mode does not control output delivery guarantees. Option B is incorrect because event ordering and late arrival policies handle timing, not delivery semantics. Option C is incorrect because checkpointing is for managing internal query state, not for ensuring exactly-once output.

Answer

Set the input to 'Exactly Once' consumption mode.

Answer

Configure event ordering and late arrival policies.

Answer

Enable checkpointing in the query.

Question 2

Your organization uses Azure Purview for data governance. You need to ensure that sensitive data is properly classified and that access to it is monitored. Which THREE actions should you take? (Choose three.)

Accepted Answer

Create custom sensitivity labels in Microsoft Purview Information Protection and apply them to data sources.. The correct answers are C, D, and E. Creating custom sensitivity labels in Microsoft Purview Information Protection (option C) allows data to be tagged with sensitivity levels. Integrating Azure Purview with Microsoft Defender for Cloud Apps (option D) provides monitoring of access to sensitive data. Automated scanning in Azure Purview (option E) discovers and classifies data automatically. Option A is incorrect because Azure Policy is used for governance and compliance enforcement, not for data classification or monitoring. Option B is incorrect because Azure Sentinel is a security information and event management (SIEM) solution, not a data classification tool.

Answer

Define Azure Policy initiatives to enforce classification on all storage accounts.

Answer

Use Azure Sentinel to classify data as it is ingested.

Question 3

You are designing a data pipeline using Azure Synapse Pipelines. The pipeline ingests data from multiple sources, performs transformations using a notebook, and loads the results into a dedicated SQL pool. You need to ensure that if the notebook fails, the entire pipeline stops and sends an alert. What is the most efficient way to configure this?

Accepted Answer

Add a 'Fail' activity after the notebook activity and connect the notebook's failure output to it. Configure the Fail activity to send an alert.. The most efficient: In Azure Synapse Pipelines (and Azure Data Factory), you can connect the failure output of the notebook activity to a 'Fail' activity. The Fail activity can be configured to send an alert (e.g., via webhook) and it stops the pipeline run immediately. Option A is incorrect because setting a wildcard error path would allow other activities to run; instead, you should explicitly route the failure to a Fail activity. Option B is incorrect because setting retry to 0 only prevents retries, but does not automatically stop the pipeline or send an alert. Option D is incorrect because the default behavior of a failed activity is to continue pipeline execution along the default error path, not to stop entirely; you must define an explicit failure path.

Answer

Set the notebook activity's error path to a webhook activity that sends an alert, and then set a wildcard error path for the pipeline.

Answer

Set the notebook activity's retry count to 0, and configure an alert on the pipeline run failure.

Answer

No configuration needed; by default, a failed activity stops the entire pipeline.

Question 4

A company uses Azure Synapse Analytics dedicated SQL pool. They notice that queries against a large fact table are slow. They have already created statistics on all columns used in WHERE clauses and JOIN predicates. What should they do next to improve query performance?

Accepted Answer

Partition the table on a frequently filtered column.. Partitioning on a frequently filtered column enables partition elimination, reducing the amount of data scanned and improving query performance. Option A (result-set caching) caches query results for repeated queries, but this question is about slow queries on a large fact table, not repeated queries. Option B (increasing DWU) might improve performance by providing more resources, but it is a more costly approach and not the best first step; partitioning is a better design optimization. Option C (creating additional statistics on all columns) is unnecessary because they already have statistics on columns used in WHERE and JOIN; statistics on all columns would waste resources and not help.

Answer

Enable result-set caching.

Answer

Increase the DWU setting for the dedicated SQL pool.

Answer

Create additional statistics on all columns.

Question 5

You need to ensure that data in an Azure Data Lake Storage Gen2 account is encrypted at rest using a customer-managed key. Which feature should you configure?

Accepted Answer

Azure Key Vault integration with Storage Service Encryption. Azure Key Vault integration with Storage Service Encryption. This feature allows ADLS Gen2 to use customer-managed keys stored in Azure Key Vault for encryption at rest. Option B is incorrect because Azure Information Protection is for data classification and labeling, not encryption. Option C is incorrect because Storage Service Encryption with Microsoft-managed keys is the default, but the requirement specifies customer-managed keys. Option D is incorrect because Azure Disk Encryption applies to VM disks, not ADLS Gen2 storage.

Answer

Azure Information Protection

Answer

Azure Storage Service Encryption with Microsoft-managed keys

Answer

Azure Disk Encryption

Question 6

An organization is using Azure Data Factory to ingest data from multiple on-premises SQL Server databases into Azure Synapse Analytics. They need to ensure that sensitive data is masked during ingestion before landing in the staging area. What is the best approach?

Accepted Answer

Use a Mapping Data Flow with derived column transformations to mask sensitive columns.. Mapping Data Flow in Azure Data Factory allows you to transform data during ingestion. Using derived column transformations, you can apply masking functions (e.g., substituting characters, hashing) to sensitive columns before writing to the staging area. This approach masks data before it reaches the staging area, meeting the requirement. Option A (Azure Policy) is for compliance and cannot mask data. Option B (Dynamic Data Masking on source) masks data at query time, not during ingestion. Option D (Azure Purview) is for data governance and classification, not for masking data during a pipeline.

Answer

Apply an Azure Policy that masks sensitive data in Azure Synapse Analytics.

Answer

Use Azure SQL Database dynamic data masking on the source databases.

Answer

Use Azure Purview to classify and mask sensitive data automatically.

Question 7

A company uses Azure Databricks for data processing. They want to monitor the performance of Spark jobs and set up alerts for job failures. Which Azure service should they use?

Accepted Answer

Azure Monitor. Azure Monitor is the central service for collecting metrics and logs from Azure Databricks, enabling performance monitoring and alerting on Spark jobs. Option A is incorrect because Azure Advisor provides recommendations but not real-time monitoring or alerts. Option B is incorrect because Azure Sentinel is a SIEM solution for security incidents, not for job performance monitoring. Option C is incorrect because Azure Log Analytics is a component of Azure Monitor used for log analysis, but the overarching service for monitoring and alerts is Azure Monitor.

Answer

Azure Advisor

Answer

Azure Sentinel

Answer

Azure Log Analytics

Question 8

You are designing a data pipeline in Azure Data Factory that copies data from Azure Blob Storage to Azure SQL Database. The data contains personally identifiable information (PII). What should you use to protect the data during transit?

Accepted Answer

Encryption over HTTPS/TLS. Azure Data Factory always encrypts data in transit using TLS. Option A is wrong because Azure Information Protection is for labeling, not transit encryption. Option C is wrong because Azure Disk Encryption is for at-rest encryption of disks. Option D is wrong because Azure Storage Service Encryption is for at-rest encryption.

Answer

Azure Information Protection

Answer

Azure Disk Encryption

Answer

Azure Storage Service Encryption

Question 9

A company uses Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage Gen2. They notice that queries are slow and want to improve performance by reducing the amount of data read. What is the most effective strategy?

Accepted Answer

Partition the data by a frequently filtered column and use file elimination in queries.. Partitioning the data in the lake and using partition elimination reduces data read. Option B is wrong because increasing the number of compute nodes is not possible in serverless; it's auto-scaling. Option C is wrong because OPENROWSET with CSV reads all files; it does not reduce data read. Option D is wrong because CETAS is for creating external tables, not for improving query performance directly.

Answer

Increase the number of compute nodes in the serverless pool.

Answer

Use OPENROWSET with CSV format instead of Parquet.

Answer

Create external tables using CETAS and query them.

Question 10

Which THREE actions can improve the performance of a dedicated SQL pool in Azure Synapse Analytics?

Accepted Answer

Partition large fact tables on a date column.. Options B, D, and E are correct. Partitioning large fact tables on a date column allows partition elimination, improving query performance. Using replicated tables for small dimension tables avoids data movement during joins. Enabling result-set caching stores query results for reuse, reducing compute load. Option A is incorrect because rowstore indexes are not optimal for analytics; columnstore indexes are preferred. Option C is incorrect because round-robin distribution is not suitable for all tables and can cause excessive data shuffling.

Answer

Use rowstore indexes instead of columnstore indexes.

Answer

Use round-robin distribution for all tables.

Question 11

Which THREE best practices should be followed when designing a data lake in Azure Data Lake Storage Gen2 for optimal performance?

Accepted Answer

Use Parquet file format for analytics workloads.. Options C, D, and E are correct. Parquet format is columnar and provides efficient compression and query performance. Partitioning by date allows partition elimination, reducing data scanned. A naming convention that avoids special characters and high cardinality prevents throttling and improves parallelism. Option A is incorrect because disabling hierarchical namespace is not a best practice; hierarchical namespace enables folder-level security and is required for ADLS Gen2. Option B is incorrect because a deep directory structure can cause performance issues due to metadata overhead; a flat or shallow structure is recommended.

Answer

Disable hierarchical namespace to improve performance.

Answer

Use a deep directory structure with many subfolders.

Question 12

Refer to the exhibit. You have an Azure Data Lake Storage Gen2 account with hierarchical namespace enabled. The exhibit shows an Azure CLI command output that lists access permissions. User user@contoso.com is a member of group-analysts@contoso.com. What level of access does user@contoso.com have to the /data/analytics/ directory?

Accepted Answer

Read and write access. RBAC permissions are additive. The user has Storage Blob Data Contributor at the root scope, which grants read/write access to all directories. The reader role at the directory scope does not override the broader contributor role. Option A is wrong because the user has write access via the root scope. Option B is wrong because the user has read access from the root scope already. Option D is wrong because the user has explicit permissions.

Answer

Access denied because of the directory-level reader role

Answer

Read-only access

Answer

No access

Question 13

Refer to the exhibit. You have an Azure Data Factory with two triggers defined as shown. The DailyTrigger runs the CopyPipeline every day at midnight UTC. The BlobTrigger runs the ProcessPipeline when a blob is created in the /input/ folder. You notice that the ProcessPipeline is not executing even though blobs are being created. What is the most likely cause?

Accepted Answer

The storage account does not have an event subscription configured for blob creation.. The most likely cause is that the storage account does not have an event subscription configured for blob creation. The BlobEventsTrigger in Azure Data Factory relies on an event subscription from the storage account to the trigger. Without this subscription, the trigger will not fire even when blobs are created in the specified path. Option D correctly identifies this issue. Option A is incorrect because the blobPathBeginsWith property includes the container name prefix; Option B is incorrect because the trigger is configured to listen to blob creation events; Option C is incorrect because the ProcessPipeline does not require parameters from the trigger.

Answer

The blobPathBeginsWith property is missing the container name.

Answer

The BlobEventsTrigger is configured to listen to the wrong event type.

Answer

The ProcessPipeline expects parameters that are not provided by the trigger.

Question 14

An organization is using Azure Synapse Analytics and wants to implement column-level security to restrict access to sensitive columns. Which feature should they use?

Accepted Answer

Column-level security using GRANT. Column-level security in Azure Synapse Analytics is implemented using GRANT statements on specific columns, restricting access to sensitive columns. Option A is incorrect because dynamic data masking obfuscates data at query time but does not prevent access. Option B is incorrect because Azure Purview is a data governance service, not for access control. Option D is incorrect because row-level security filters rows, not columns.

Answer

Dynamic data masking

Answer

Azure Purview

Answer

Row-level security

Question 15

A company uses Azure Stream Analytics to process real-time data from IoT devices. They need to ensure that the output to Azure Synapse Analytics is optimized for high throughput and low latency. What should they configure in the Stream Analytics job?

Accepted Answer

Partition the output by a key and use a columnstore index in the target table.. Partitioning the output by a key enables parallel writes to Azure Synapse Analytics, significantly improving throughput, and using a columnstore index optimizes the target table for large data loads, reducing latency. Option A is incorrect because the question specifically requires Azure Synapse Analytics output, not Azure SQL Database. Option C is wrong because a single partition serializes writes, reducing throughput. Option D is incorrect because disabling batching increases the number of write transactions, leading to higher latency and reduced throughput.

Answer

Use Azure SQL Database output instead of Azure Synapse Analytics.

Answer

Use a single partition for the output to simplify processing.

Answer

Disable batching to reduce latency.