DP-203 Practice Test 1 — 10 Questions

Question 1

Drag and drop the steps to set up Azure Purview for data cataloging and lineage tracking into the correct order.

Accepted Answer

Create Azure Purview account, then Register data sources, then Scan data sources, then Set classifications, then Explore catalog. First create the Purview account, then register sources, scan them, set classifications, and finally explore the catalog.

Answer

Create Azure Purview account, then Register data sources, then Set classifications, then Scan data sources, then Explore catalog

Answer

Register data sources, then Create Azure Purview account, then Scan data sources, then Set classifications, then Explore catalog

Answer

Create Azure Purview account, then Scan data sources, then Register data sources, then Set classifications, then Explore catalog

Question 2

A data engineer is designing a solution to store historical sales data for a retail company. The data is append-only and accessed infrequently for compliance reports. The solution must minimize storage costs while allowing retrieval within 24 hours. Which storage tier should be used for the data?

Accepted Answer

Cool tier. The Cool tier is the correct choice because it is optimized for data that is infrequently accessed and stored for at least 30 days, offering low storage costs with retrieval times in the range of seconds to hours, which meets the 24-hour retrieval requirement. The data is append-only and used for compliance, so the Cool tier balances cost and accessibility without the high retrieval costs or long rehydration delays of the Archive tier.

Answer

Hot tier

Answer

Archive tier

Answer

Premium tier

Question 3

You are designing a batch processing solution using Azure Databricks. The data source is a large Parquet dataset stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The processing requires joining two datasets: one with 10 billion rows and another with 1 million rows. The cluster uses Photon runtime. Which optimization should you apply to minimize shuffle?

Accepted Answer

Broadcast the smaller table (1 million rows) to all worker nodes.. Broadcasting the smaller table (1 million rows) to all worker nodes is the correct optimization because it eliminates the need for a full shuffle during the join. With Photon runtime, broadcast joins are highly efficient as they replicate the small table to each executor, allowing map-side joins that avoid costly data movement across the network. Given the 10:1 row ratio, the 1-million-row table is well within the default broadcast threshold (10 MB compressed, configurable via spark.sql.autoBroadcastJoinThreshold), making this the most effective shuffle-minimization technique.

Answer

Increase the cluster size to reduce shuffle overhead.

Answer

Create bucketed tables on the join key for both datasets.

Answer

Use Delta Lake and optimize file layout with OPTIMIZE command.

Question 4

You are developing a data processing solution in Azure Synapse Analytics. The solution must use a serverless SQL pool to query Parquet files stored in Azure Data Lake Storage Gen2. Which authentication method should you use to ensure that the queries use the identity of the caller and adhere to Azure role-based access control (RBAC) permissions?

Accepted Answer

Microsoft Entra ID pass-through authentication.. Microsoft Entra ID pass-through authentication (option A) is correct because it allows the serverless SQL pool to use the caller's identity when accessing Azure Data Lake Storage Gen2. This ensures that Azure RBAC permissions (e.g., Storage Blob Data Reader) assigned to the user are evaluated for each query, providing fine-grained access control without exposing storage account keys or tokens.

Answer

Storage account key.

Answer

Shared access signature (SAS) token.

Answer

Service principal with a secret.

Question 5

You need to transform semi-structured JSON data into a tabular format for analysis in Azure Synapse Analytics. The data is stored in ADLS Gen2. Which feature should you use to query the JSON data directly without loading it into a table?

Accepted Answer

Use the OPENROWSET function in a serverless SQL pool.. OPENROWSET in Synapse serverless SQL can query JSON files directly. Option B (Azure Data Factory) flattens JSON but is an orchestration tool, not a direct query method. Option C (PolyBase) requires external tables. Option D (COPY INTO) loads data into a table, not direct query.

Answer

Use Azure Data Factory to flatten the JSON and store as Parquet.

Answer

Create an external table using PolyBase.

Answer

Use the COPY INTO command in a dedicated SQL pool.

Question 6

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 7

A company is designing a data storage solution for IoT device telemetry. Each device sends a JSON payload every second. The data must be stored in a way that supports real-time dashboards and long-term analytics with low latency. Which Azure data store should be used for the ingestion layer?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice for the ingestion layer because it is a fully managed, real-time data streaming platform designed to ingest millions of events per second with low latency. It supports the capture of JSON telemetry from IoT devices and integrates directly with downstream analytics services like Azure Stream Analytics for real-time dashboards and long-term storage in Azure Data Lake or Blob Storage. Its partitioned throughput model ensures scalable, durable ingestion without blocking producers.

Answer

Azure SQL Database

Answer

Azure Blob Storage

Answer

Azure Data Lake Storage

Question 8

Match each Azure data integration tool to its typical use case.

Accepted Answer

Azure Data Factory: Orchestrate and automate data movement and transformation at scale.. In this matching exercise, the correct pairs are: Azure Data Factory for orchestration, Azure Synapse Analytics for data warehousing, Azure Databricks for data engineering/ML, and Azure Stream Analytics for real-time streaming. Common confusions involve swapping orchestration and streaming roles.

Answer

Azure Data Factory: Process real-time streaming data from IoT devices.

Answer

Azure Synapse Analytics: Orchestrate data movement at scale.

Question 9

You are building a real-time dashboard to monitor user activity on a website. The data is ingested via Azure Event Hubs and must be aggregated every minute with a 30-second late-arrival tolerance. The aggregated results should be stored in Azure Cosmos DB for low-latency reads. Which Azure service should you use to perform the windowed aggregation?

Accepted Answer

Azure Stream Analytics with a tumbling window of 1 minute and a late-arrival policy of 30 seconds.. Azure Stream Analytics is the correct choice because it natively supports windowed aggregations (tumbling, hopping, sliding, session) and allows you to define a late-arrival policy to handle out-of-order events. A tumbling window of 1 minute with a late-arrival tolerance of 30 seconds meets the requirement exactly, and the output can be directly written to Azure Cosmos DB for low-latency reads.

Answer

Azure Functions triggered by Event Hubs to aggregate data and write to Cosmos DB.

Answer

Azure Databricks with structured streaming and a sliding window.

Answer

Azure Analysis Services to process streaming data directly from Event Hubs.

Question 10

Which TWO options are recommended bulk loading methods for Azure Synapse SQL Pool? (Choose two.)

Accepted Answer

Using the COPY statement. For Azure Synapse SQL Pool, the recommended bulk loading methods are the COPY statement and PolyBase. BCP and SSIS are supported for some scenarios, but they are not the recommended bulk loading methods for large-scale data loads. INSERT INTO VALUES is row-by-row and is not recommended for bulk loading.

Answer

Using INSERT INTO VALUES

Answer

Using BCP utility

Answer

Using SQL Server Integration Services (SSIS)