Microsoft Azure Data Engineer Associate DP-203 practice test

You are designing a data storage solution for IoT sensor data. The data is written thousands of times per second and requires low-latency reads for real-time dashboards. Which Azure storage solution should you use?

Trap 1: Azure Blob Storage

Blob Storage is optimized for large, unstructured data with higher latency, not real-time ingestion.

Trap 2: Azure SQL Database

SQL Database can handle writes but may struggle with the scale and low-latency requirements of IoT sensor data.

Trap 3: Azure Data Lake Storage Gen2

Designed for big data analytics, not real-time ingestion and query.

A
Azure Blob Storage
Why wrong: Blob Storage is optimized for large, unstructured data with higher latency, not real-time ingestion.
C
Azure SQL Database
Why wrong: SQL Database can handle writes but may struggle with the scale and low-latency requirements of IoT sensor data.
D
Azure Data Lake Storage Gen2
Why wrong: Designed for big data analytics, not real-time ingestion and query.

Read the full NAT/PAT explanation →

Question 2easymultiple choice

A data processing job in Azure Synapse Analytics writes results to a table in the dedicated SQL pool. After a failure, the job restarts from the beginning, causing duplicates. Which design pattern should you implement to ensure idempotent writes?

Trap 1: Use a TRUNCATE statement before each insert.

Not atomic; duplicates can occur if job fails after truncate.

Trap 2: Use a MERGE statement with a unique key to upsert data.

May still cause duplicates if source has same key multiple times.

Trap 3: Use CREATE TABLE AS SELECT (CTAS) with a unique constraint.

CTAS doesn't handle restarts well.

A
Use a TRUNCATE statement before each insert.
Why wrong: Not atomic; duplicates can occur if job fails after truncate.
B
Use a MERGE statement with a unique key to upsert data.
Why wrong: May still cause duplicates if source has same key multiple times.
C
Use a staging table and then swap partitions with the target table.
Atomic swap ensures idempotency.
D
Use CREATE TABLE AS SELECT (CTAS) with a unique constraint.
Why wrong: CTAS doesn't handle restarts well.

Read the full NAT/PAT explanation →

Question 3hardmultiple choice

A multinational corporation uses Azure Data Lake Storage Gen2 to store petabytes of parquet files partitioned by date and hour. Data scientists report that queries on the last 7 days of data take over 30 minutes, while queries on older data are fast. The storage account uses the default Azure Blob Storage hierarchical namespace. Which action will MOST improve query performance on recent data?

Trap 1: Convert the parquet files to CSV format to reduce metadata overhead

CSV is less efficient than parquet for analytical queries.

Trap 2: Enable soft delete on the storage account to reduce read latency

Soft delete does not affect read performance.

Trap 3: Apply Z-order clustering on the parquet files using Azure Databricks

Z-order improves within-file skipping, but the issue is partition pruning.

A
Convert the parquet files to CSV format to reduce metadata overhead
Why wrong: CSV is less efficient than parquet for analytical queries.
B
Enable soft delete on the storage account to reduce read latency
Why wrong: Soft delete does not affect read performance.
C
Optimize the partition layout by partitioning by date first, then by hour, to reduce the number of partitions scanned for recent data
Recent data queries scan fewer partitions, improving performance.
D
Apply Z-order clustering on the parquet files using Azure Databricks
Why wrong: Z-order improves within-file skipping, but the issue is partition pruning.

Question 4mediummulti select

You are designing a data processing solution in Azure that must handle both batch and streaming data. The solution should use a common storage layer for both and support schema evolution. Which TWO technologies should you recommend?

Trap 1: Azure SQL Database

Not designed for streaming.

Trap 2: Apache Kafka on HDInsight

Less integrated with Azure ecosystem.

Trap 3: Azure Data Lake Storage Gen2

Storage layer, not processing.

A
Azure Event Hubs
Common ingestion for batch and streaming.
B
Azure SQL Database
Why wrong: Not designed for streaming.
C
Apache Kafka on HDInsight
Why wrong: Less integrated with Azure ecosystem.
D
Delta Lake (on Azure Databricks)
Supports batch and streaming, schema evolution.
E
Azure Data Lake Storage Gen2
Why wrong: Storage layer, not processing.

Question 5easymultiple choice

A company ingests streaming data from IoT devices into Azure Event Hubs. The data must be processed in near real-time to detect anomalies and stored in Azure Data Lake Storage Gen2 for historical analysis. The solution must minimize latency and avoid duplicate processing. Which Azure service should be used for processing?

Trap 1: Azure Data Factory

Azure Data Factory is for batch orchestration, not real-time streaming.

Trap 2: Azure Databricks with Structured Streaming

Azure Databricks can process streams but has higher latency and complexity compared to Stream Analytics for this scenario.

Trap 3: Azure Functions with Event Hubs trigger

Azure Functions can process events but lacks built-in stream processing features like windowing and exactly-once.

A
Azure Data Factory
Why wrong: Azure Data Factory is for batch orchestration, not real-time streaming.
B
Azure Databricks with Structured Streaming
Why wrong: Azure Databricks can process streams but has higher latency and complexity compared to Stream Analytics for this scenario.
C
Azure Functions with Event Hubs trigger
Why wrong: Azure Functions can process events but lacks built-in stream processing features like windowing and exactly-once.
D
Azure Stream Analytics
Azure Stream Analytics provides low-latency stream processing with exactly-once semantics and integrates with Event Hubs and Data Lake Storage.

Question 6mediummulti select

Which TWO actions are appropriate when designing a data processing solution that must meet strict SLAs for latency and throughput?

Trap 1: Process all data synchronously to ensure consistency

Synchronous processing increases latency.

Trap 2: Use a single large cluster for all workloads to simplify management

May cause resource contention and doesn't meet SLAs.

Trap 3: Use a single node for orchestration to reduce complexity

Single node can become a bottleneck.

A
Partition data by date and hour to improve query performance
Partitioning reduces data scanned and improves throughput.
B
Implement Auto-Tune for Spark workloads in Azure Synapse Analytics
Auto-Tune optimizes performance for varying workloads.
C
Process all data synchronously to ensure consistency
Why wrong: Synchronous processing increases latency.
D
Use a single large cluster for all workloads to simplify management
Why wrong: May cause resource contention and doesn't meet SLAs.
E
Use a single node for orchestration to reduce complexity
Why wrong: Single node can become a bottleneck.