DP-203 Practice Test 21 — 15 Questions

Question 1

You are designing a data processing solution that must handle both batch and stream processing in a unified manner. You need to choose a technology that supports a single code base for both processing modes. Which TWO technologies meet this requirement?

Accepted Answer

Azure Databricks with Delta Live Tables. Option B (Azure Databricks with Delta Live Tables) and Option D (Apache Spark Structured Streaming in Azure Synapse) support a unified batch-stream programming model. Option A (Azure Stream Analytics) is streaming-only. Option C (Azure Data Factory) is batch-only. Option E (Azure SQL Database) is not a processing engine.

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Answer

Azure SQL Database with Change Tracking

Question 2

Your company uses Azure Data Lake Storage Gen2 as the central data lake. You need to process batch data using serverless Spark jobs that can be scheduled daily. Which Azure service should you use?

Accepted Answer

Azure Synapse Analytics serverless Spark pool with pipelines.. Option C is correct because Azure Synapse Analytics provides serverless Spark pools with a built-in scheduler. Option A (Azure Databricks) is not serverless (requires cluster management). Option B (Azure Batch) is for custom compute jobs. Option D (Azure Machine Learning) is for ML workloads.

Answer

Azure Batch with custom Spark containers.

Answer

Azure Databricks with a job cluster.

Answer

Azure Machine Learning with Spark compute.

Question 3

Refer to the exhibit. You created an external table in Azure Synapse Analytics serverless SQL pool to query Parquet files. Queries return no rows even though the files exist. What is the most likely issue?

Accepted Answer

The LOCATION path in the external table is relative to the data source, but the data source points to the wrong container or folder. Option B is correct. The external table LOCATION is 'parquet/sales/' but the external data source points to the root 'sales' container. The combined path is 'sales/parquet/sales/', which may be wrong. Option A is wrong because compression is supported. Option C is wrong because credential is defined. Option D is wrong because the file format is correct.

Answer

The CREDENTIAL is missing

Answer

The FILE_FORMAT is incorrectly specified

Answer

The DATA_COMPRESSION is not supported for Parquet

Question 4

You are developing a real-time data processing solution using Azure Stream Analytics. The input is from an Azure Event Hub, and the output is to an Azure Synapse Analytics dedicated SQL pool. You need to ensure exactly-once delivery semantics to the SQL pool. What should you configure?

Accepted Answer

Use the Azure Synapse Analytics output adapter with exactly-once semantics.. Option D is correct because Stream Analytics offers exactly-once output to Azure Synapse via the dedicated SQL pool output adapter. Option A is wrong because checkpointing is for recovery, not exactly-once. Option B is wrong because event ordering is about sequence, not delivery guarantees. Option C is wrong because custom deserializers do not provide exactly-once.

Answer

Enable checkpointing in the query.

Answer

Use a custom deserializer.

Answer

Configure event ordering by timestamp.

Question 5

You are designing a real-time data processing solution using Azure Stream Analytics. The input is from Azure Event Hubs, and the output is to Azure Synapse Analytics. The solution must guarantee exactly-once delivery to Synapse. Which THREE configurations are required? (Choose three.)

Accepted Answer

Define a watermark strategy in the query to handle late-arriving events.. Options A, C, and E are correct. Exactly-once semantics require a unique identifier for deduplication, a watermark to handle late events, and output to a table with a primary key for upsert. Option B is incorrect because late arrival tolerance is not required for exactly-once. Option D is incorrect because batch mode is not supported for exactly-once output to Synapse.

Answer

Configure the output to use batch mode for writing.

Answer

Set the late arrival tolerance window to zero.

Question 6

Which TWO actions can you take to optimize query performance in Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

Use replicated tables for small dimension tables. Correct: A and D. Using replicated tables avoids data movement for small dimension tables, and materialized views pre-compute aggregations. B is wrong because hash distribution on a high-cardinality column is generally good. C is wrong because increasing DWU improves performance but is not a design optimization; it's a scaling action. E is wrong because round-robin distribution is not optimal for star schemas.

Answer

Use hash distribution on a low-cardinality column

Answer

Use round-robin distribution for fact tables

Answer

Increase the DWU setting after every query

Question 7

Refer to the exhibit. You submit a Spark job in Azure Synapse Analytics using the Azure CLI. The job runs slowly during the shuffle phase. The input data is about 200 GB. Which configuration change would best improve performance for this shuffle-heavy workload?

Accepted Answer

Increase spark.sql.shuffle.partitions to 800.. Option B is correct because for a 200 GB input, 400 shuffle partitions may be too few, causing large partitions and slow shuffles. Increasing partitions to a higher number (e.g., 800 or more) can improve parallelism. Option A is wrong because executor size 'Small' might be insufficient; increasing executor size could help but the question asks about configuration change, and partition count is more directly related to shuffle performance. Option C is wrong because decreasing partitions would make partitions larger. Option D is wrong because executor count of 2 is low; increasing would help but the partition count is the key issue for shuffle.

Answer

Increase the number of executors to 4.

Answer

Change executor size to 'Large' to increase memory per executor.

Answer

Decrease spark.sql.shuffle.partitions to 200 to reduce overhead.

Question 8

You have a mission-critical pipeline that processes financial transactions in Azure Synapse Analytics. The pipeline uses Azure Data Factory with a mapping data flow to transform data. You need to ensure high availability and minimal data loss in case of a regional failure. What should you implement?

Accepted Answer

Use Azure Synapse Link for Cosmos DB to enable near real-time analytics with multi-region writes.. Option D is correct because Azure Synapse Link for Cosmos DB provides near real-time sync with automatic failover. Option A (active geo-replication for SQL pool) is not native to Synapse. Option B (manual restore) has data loss. Option C (backup to GRS) does not provide automatic failover.

Answer

Store the source data in a GRS storage account and use Azure Data Factory to copy from the secondary endpoint.

Answer

Configure the pipeline to retry on failure and manually restore from backup.

Answer

Use Azure SQL Database active geo-replication as the source.

Question 9

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?

Accepted Answer

Azure Stream Analytics. Option A is correct because 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 10

You are designing a batch processing solution for a financial services company that processes transactions from multiple sources. The data is stored in Azure Data Lake Storage Gen2 in Parquet format. You need to perform complex transformations including joins, aggregations, and window functions, and then load the results into an Azure Synapse Analytics dedicated SQL pool. The transformations must be written in SQL and executed on a serverless Spark cluster to minimize costs. You want to manage the code in a Git repository and automate the deployment using Azure DevOps. Which approach should you recommend?

Accepted Answer

Use a serverless SQL pool in Azure Synapse to query the Parquet files using T-SQL, then use CETAS to write the results to the dedicated SQL pool.. Option B is correct because Synapse Serverless SQL pool can query Parquet files directly using T-SQL, and you can use CREATE EXTERNAL TABLE AS SELECT (CETAS) to write results to the dedicated SQL pool. This avoids Spark costs and uses serverless compute. Option A uses Spark, which costs more. Option C uses Azure Databricks, which is external and adds complexity. Option D uses Azure Data Factory with Data Flows that run on Spark, also incurring costs.

Answer

Use Azure Databricks with Auto Loader to read from Data Lake Storage, perform transformations using Spark SQL, and write to the dedicated SQL pool via JDBC.

Answer

Use Azure Data Factory with Mapping Data Flows to visually design transformations and write to the dedicated SQL pool.

Answer

Create a Spark job definition in Azure Synapse that reads Parquet files, performs transformations using PySpark, and writes to the dedicated SQL pool using the Spark Synapse connector.

Question 11

You are monitoring a production data pipeline in Azure Data Factory that runs hourly. You notice that the pipeline has been failing for the last 3 hours due to a timeout error when writing to the sink Azure SQL Database. The database is a General Purpose tier with 100 DTUs. The pipeline writes approximately 1 million rows per run. What is the most effective way to prevent the timeout?

Accepted Answer

Increase the writeBatchSize in the sink settings to reduce the number of batch inserts.. Option D is correct because increasing the batch size reduces the number of round trips, which can speed up the write operation and reduce timeout risk. Option A is wrong because reducing parallelism might increase duration. Option B is wrong because increasing DTUs helps but is more costly and may not be necessary. Option C is wrong because using a staging blob could add overhead and still timeout.

Answer

Reduce the degree of copy parallelism in the copy activity.

Answer

Enable staged copy using Azure Blob Storage as an interim store.

Answer

Scale up the Azure SQL Database to a higher DTU tier.

Question 12

Refer to the exhibit. You deploy this ARM template to create a managed integration runtime in Azure Synapse Analytics. You notice that the integration runtime shows as 'Running' but copy activities using it are slow. The data volume is 500 GB per run. What is the most likely cause of the poor performance?

Accepted Answer

The node size (Standard_D3_v2) is too small for the data volume.. Option B is correct because the node size Standard_D3_v2 has limited memory and CPU for large data volumes. A larger node size (e.g., Standard_D8_v3) would improve performance. Option A is wrong because 2 nodes is reasonable; increasing nodes could help but the node size is more critical. Option C is wrong because the location 'AutoResolve' is fine. Option D is wrong because the integration runtime type 'Managed' is appropriate for Azure-based copy.

Answer

The integration runtime type should be 'Self-hosted' for better performance.

Answer

The number of nodes (2) is insufficient for 500 GB of data.

Answer

The location is set to 'AutoResolve' which may cause data movement across regions.

Question 13

Your team is using Azure Data Factory to orchestrate a data pipeline that copies data from an on-premises SQL Server to Azure Blob Storage. The pipeline runs successfully during testing. However, after moving to production, you notice that the pipeline fails intermittently with connectivity errors. You need to ensure reliable data transfer. What should you implement?

Accepted Answer

Use Self-Hosted Integration Runtime. Option B is correct because a self-hosted integration runtime provides a gateway for on-premises connectivity. Option A is wrong because Azure IR cannot access on-premises networks. Option C is wrong because VPN is not a direct solution for data factory connectivity. Option D is wrong because PolyBase is for loading into Synapse, not for connectivity.

Answer

Use Azure Integration Runtime (IR)

Answer

Use PolyBase for data transfer

Answer

Configure Azure VPN Gateway

Question 14

You are designing a data processing pipeline in Azure Data Factory that ingests data from an on-premises SQL Server database to Azure Data Lake Storage Gen2. The data volume is large (500 GB). The network connection between on-premises and Azure is limited to 100 Mbps. You need to minimize the time to transfer the initial full load while ensuring data integrity. Which approach should you recommend?

Accepted Answer

Use Azure Data Box to copy the data offline. Option D is correct because Azure Data Box physically ships the data, bypassing network bandwidth limitations for large initial loads. Option A is wrong because it would take over 11 hours even at full bandwidth, and network may not be stable. Option B is wrong because it compresses but still uses network. Option C is wrong because VPN adds overhead.

Answer

Use Azure Data Factory copy activity with parallel connections

Answer

Use Azure ExpressRoute to increase bandwidth

Answer

Compress the data using GZip and use copy activity

Question 15

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, C, and E 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 is wrong because Azure Batch is for large-scale parallel computing, not event-driven triggers. Option D is wrong because Azure Logic Apps is more for orchestration, not custom code execution.

Answer

Azure Batch

Answer

Azure Logic Apps