DP-203 Practice Test 16 — 15 Questions

Question 1

Your organization uses Azure Synapse Analytics. You need to design a data transformation pipeline that processes streaming data from Azure Event Hubs, performs aggregations over a 5-minute tumbling window, and loads the results into a dedicated SQL pool table. Which Azure service should you use to implement the streaming transformation?

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

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the appropriate service for real-time stream processing with windowed aggregations. Option B is wrong because Azure Data Factory is for batch orchestration. Option C is wrong because Spark Structured Streaming is for big data workloads but less integrated with SQL pools. Option D is wrong because Azure Functions is not designed for streaming windowed aggregations.

Answer

Azure Data Factory

Answer

Apache Spark for Azure Synapse

Answer

Azure Functions

Question 2

Which THREE Azure services can you use together to build a serverless, event-driven data processing pipeline that ingests data from Azure Blob Storage, transforms it using custom code, and loads it into Azure Cosmos DB?

Accepted Answer

Azure Functions. Options A, B, and D are correct. Azure Functions can be triggered by Blob Storage events to run custom code, and the output can be written to Cosmos DB via the Cosmos DB output binding (option B). Azure Event Grid (option D) enables event-driven triggers for the pipeline. Option C (Azure Batch) is wrong because it is designed for large-scale parallel computing, not event-driven triggers. Option E (Azure Logic Apps) is wrong because it is more for workflow orchestration with connectors, not custom code execution.

Answer

Azure Batch

Answer

Azure Logic Apps

Question 3

Which TWO options are correct about using Azure Stream Analytics for real-time data processing?

Accepted Answer

You can join streaming data with static reference data for enrichment.. Correct: A (reference data joins for enrichment) and B (windowing functions for time-based aggregations). Option C is wrong because while Stream Analytics supports many output sinks, not all Azure data stores are supported out-of-the-box; some require custom code or connectors. Option D is wrong because Stream Analytics provides at-least-once processing semantics, not exactly-once; exactly-once depends on the sink configuration and output adapter. Option E is incorrect because Stream Analytics natively supports custom JavaScript functions for complex transformations, but it does not natively support C# functions; C# functions require integration with Azure Functions, making this option misleading.

Answer

Stream Analytics can output to any Azure data store without custom code.

Answer

Stream Analytics guarantees exactly-once processing semantics.

Answer

You can use custom JavaScript or C# functions for complex transformations.

Question 4

Which THREE considerations are important when designing a data processing solution using Azure Databricks for ETL workloads? (Select three.)

Accepted Answer

Auto-scaling of compute resources. Auto-scaling of compute resources ensures that the cluster can adjust to workload demands, optimizing cost and performance. Option B is correct because Delta Lake provides ACID transactions, schema enforcement, and optimization features that enhance data reliability in ETL pipelines. Option E is correct because proper cluster configuration and autoscaling are essential for managing resources efficiently. Option C is incorrect because Azure Databricks uses a pay-as-you-go model, not a fixed pricing model, making it more flexible. Option D is incorrect because minimum network bandwidth is not a typical design consideration for Databricks ETL workloads, as network performance is generally sufficient.

Answer

Fixed pricing model

Answer

Minimum network bandwidth

Question 5

You need to process streaming data from Azure Event Hubs and store the results in Azure Cosmos DB for a real-time dashboard. The solution must handle duplicate events and ensure exactly-once processing. Which Azure service should you use?

Accepted Answer

Azure Stream Analytics. (Azure Stream Analytics) is correct because it provides exactly-once processing when configured with Event Hubs and Cosmos DB output. Option A (Azure Data Factory) is batch-oriented. Option B (Azure Functions) may have at-least-once guarantees. Option D (Azure Databricks) can achieve exactly-once but requires more configuration.

Answer

Azure Data Factory

Answer

Azure Functions with Event Hubs trigger

Answer

Azure Databricks Structured Streaming

Question 6

You are implementing a data processing solution in Azure Databricks. The solution must read data from Azure Data Lake Storage Gen2, transform it using PySpark, and write the results back to a different location in the same storage account. You need to authenticate to the storage account securely without storing secrets in the notebook. What should you use?

Accepted Answer

Azure Key Vault-backed secret scope. Azure Key Vault-backed secret scopes allow you to securely reference secrets without storing them in the notebook. Option A is wrong because storing a client secret directly in the notebook is insecure. Option C is wrong because SAS tokens can be exposed and are less secure. Option D is wrong because storage account access keys are long-lived and should not be used in notebooks.

Answer

Service Principal with a client secret stored in the notebook

Answer

Shared access signature (SAS) token

Answer

Storage account access key

Question 7

You are tuning a Spark job in Azure Synapse Analytics that processes large Parquet files. The job currently takes too long due to data skew. Which three actions can improve performance? (Choose three.)

Accepted Answer

Use bucketing on the join key when writing intermediate data.. Options A, B, and E are correct. Repartitioning redistributes data. Salting adds a random key to break skew. Bucketing pre-partitions data. Option C is wrong because coalesce reduces partitions but does not address skew. Option D is wrong because increasing executor memory may help but does not directly solve skew.

Answer

Use coalesce to reduce the number of partitions.

Answer

Increase executor memory to handle larger partitions.

Question 8

Which THREE components are required to implement a modern data warehouse architecture on Microsoft Azure using Azure Synapse Analytics?

Accepted Answer

Microsoft Purview for data governance and lineage.. Microsoft Purview is required for data governance and lineage in a modern data warehouse architecture on Azure because it provides a unified data governance service that helps manage and monitor data across on-premises, multi-cloud, and SaaS sources. It enables automated data discovery, classification, and lineage tracking, which are essential for compliance and data quality in a modern analytics environment.

Answer

Power BI as the data visualization layer.

Answer

Azure Analysis Services for semantic modeling.

Question 9

You have a streaming pipeline using Azure Stream Analytics that ingests data from Event Hubs and outputs to Azure Synapse Analytics. The job has a high watermark delay and is falling behind. You need to reduce the latency. Which action should you take?

Accepted Answer

Increase the number of Streaming Units (SUs) for the Stream Analytics job.. Increasing the number of Streaming Units (SUs) for the Stream Analytics job allocates more compute resources, reducing latency. Adding more Event Hubs partitions may improve throughput but not directly reduce latency if the job is already bottlenecked. Switching to reference data input does not help. Using Azure Functions for output may add overhead.

Answer

Add more partitions to the Event Hubs.

Answer

Replace the output with Azure Functions for each event.

Answer

Change the input to a reference data input.

Question 10

A data engineer is building a pipeline in Azure Data Factory to load data from Azure Blob Storage to Azure Synapse Analytics using PolyBase. The pipeline must handle large files efficiently and minimize load time. Which TWO actions should the engineer take? (Choose two.)

Accepted Answer

Split large files into multiple smaller files (e.g., 256 MB each).. To optimize PolyBase loads for large files, splitting files into smaller chunks (e.g., 256 MB) enables parallel processing and reduces load time. Configuring PolyBase with the 'RejectValue' option allows handling of bad rows without failing the entire load, improving resilience. Option B (using a staging table) is not a PolyBase-specific optimization; staging is used for other loading methods like COPY INTO. Option C (converting to Parquet) is not directly related to PolyBase performance and is not required. Option D (copy activity with staging) may use PolyBase but the question asks for PolyBase-specific actions, and staging is not a PolyBase feature. Therefore, the correct actions are A and E.

Answer

Use a staging table to stage the data before loading.

Answer

Convert files to Parquet format for better compression.

Answer

Use the copy activity with staging enabled.

Question 11

You are monitoring an Azure Synapse Analytics dedicated SQL pool and notice that some queries are taking longer than expected. You need to identify queries that are experiencing significant memory pressure. Which dynamic management view (DMV) should you query?

Accepted Answer

sys.dm_pdw_exec_requests. Sys.dm_pdw_exec_requests provides information about queries including their memory grant size, which can be used to identify queries experiencing memory pressure. Option B is incorrect because sys.dm_pdw_wait_stats shows wait statistics, not memory pressure. Option C is incorrect because sys.dm_pdw_query_stats_xe is for extended events, not directly for memory grants. Option D is incorrect because sys.dm_pdw_nodes_os_performance_counters shows OS-level performance counters, not query-level memory information.

Answer

sys.dm_pdw_wait_stats

Answer

sys.dm_pdw_query_stats_xe

Answer

sys.dm_pdw_nodes_os_performance_counters

Question 12

You are optimizing the performance of an Azure Synapse Analytics dedicated SQL pool. Which TWO actions can help reduce data movement during query execution?

Accepted Answer

Use replicated tables for small dimension tables.. Options B and E are correct. Using replicated tables for small dimension tables avoids data movement during joins because the table is copied to all distributions. Distributing fact tables on the join key columns ensures that matching rows are co-located, reducing shuffling. Option A is incorrect: using hash distribution on a column not used in joins increases data movement because data is redistributed unnecessarily. Option C is incorrect: round-robin distribution distributes rows evenly but often requires data movement for joins. Option D is incorrect: increasing the resource class allocates more resources but does not directly reduce data movement.

Answer

Use hash distribution on a column that is not used in joins.

Answer

Use round-robin distribution for large fact tables.

Answer

Increase the resource class for the loading user.

Question 13

You need to monitor the health of your Azure Data Factory pipelines and set up alerts for failures. Which Azure service should you use to collect and analyze pipeline run logs?

Accepted Answer

Azure Monitor. Azure Monitor is the primary service for collecting and analyzing pipeline run logs and metrics in Azure Data Factory. Option A is wrong because Azure Purview is a data governance service, not for monitoring. Option B is wrong because Azure Log Analytics is a component of Azure Monitor used for querying logs, but Azure Monitor itself is the service that collects and analyzes the logs. Option D is wrong because Azure Sentinel is a SIEM (Security Information and Event Management) solution, not for pipeline monitoring.

Answer

Azure Purview

Answer

Azure Log Analytics

Answer

Azure Sentinel

Question 14

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 15

You need to secure access to an Azure Data Lake Storage Gen2 account. Which THREE methods can you use to authenticate and authorize access?

Accepted Answer

Shared access signatures (SAS).. Shared access signatures (SAS), access control lists (ACLs), and Azure RBAC roles are all valid methods to authenticate and authorize access to Azure Data Lake Storage Gen2. SAS tokens provide delegated access, ACLs control permissions at the file/directory level, and RBAC roles provide role-based permissions at the storage account or container level. SQL connection strings are used for Azure SQL Database, not ADLS Gen2. Managed identities are identities, not authorization methods; they can be used with RBAC but are not a direct method themselves.

Answer

SQL connection strings.

Answer

Managed identities.