DP-203 Practice Test 10 — 10 Questions

Question 1

A company is running a Spark job on Azure Databricks that processes 500 GB of data daily. The job frequently fails with 'OutOfMemoryError' during shuffles. The cluster uses 10 workers of type Standard_DS3_v2 (14 GB memory each). Which configuration change should you make to improve stability without over-provisioning?

Accepted Answer

Set spark.sql.shuffle.partitions to a higher value, e.g., 500.. The 'OutOfMemoryError' during shuffles indicates that individual partitions are too large for the executor memory. Increasing `spark.sql.shuffle.partitions` to 500 reduces the amount of data per partition, lowering memory pressure during shuffle operations. This directly addresses the error without adding more hardware.

Answer

Increase the driver memory to 28 GB.

Answer

Increase the number of workers to 20.

Answer

Reduce spark.sql.shuffle.partitions to 100.

Question 2

You need to design a near-real-time data processing solution that ingests IoT telemetry data from millions of devices. The data must be aggregated per minute and stored in Azure Cosmos DB for low-latency queries. Which Azure service combination should you use?

Accepted Answer

Azure Event Hubs -> Azure Stream Analytics -> Azure Cosmos DB. Option B is correct because Azure Stream Analytics provides native, low-latency windowed aggregation (e.g., TumblingWindow for per-minute aggregates) directly on data ingested from Event Hubs, and it has a built-in output sink to Azure Cosmos DB. This combination meets the near-real-time requirement without needing an intermediate compute or storage layer, minimizing end-to-end latency.

Answer

Azure Event Hubs -> Azure HDInsight (Kafka) -> Azure Cosmos DB

Answer

Azure IoT Hub -> Azure Databricks (Structured Streaming) -> Azure Cosmos DB

Answer

Azure Event Hubs -> Azure Data Factory -> Azure Cosmos DB

Question 3

Which THREE components are required to implement a real-time data processing solution using Azure Stream Analytics?

Accepted Answer

An input source such as Azure Event Hubs or IoT Hub. Option C is correct because Azure Stream Analytics requires a streaming input source to ingest real-time data. Azure Event Hubs and IoT Hub are the primary services that provide high-throughput, low-latency event ingestion, which Stream Analytics can consume via its built-in connector. Without a streaming input, the job cannot process real-time data.

Answer

Power BI as the output sink

Answer

Azure Data Factory pipeline for orchestration

Question 4

You are building a data processing pipeline in Azure Synapse Analytics that ingests data from Azure Blob Storage and writes to a dedicated SQL pool. You need to ensure the pipeline can handle schema changes in the source data without failing. Which TWO actions should you take?

Accepted Answer

Enable schema drift in a Mapping Data Flow activity.. Mapping Data Flows in Azure Synapse Analytics support schema drift, which allows the pipeline to dynamically handle changes in source data structure (e.g., new columns, changed data types) without failing. By enabling schema drift, the data flow can automatically detect and propagate these changes downstream, ensuring resilience against schema evolution.

Answer

Use staged copy with an intermediate storage.

Answer

Define a fixed schema in the source dataset.

Answer

Use PolyBase to load data into the dedicated SQL pool.

Question 5

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

Question 6

Match each Azure Synapse Analytics component to its function.

Question 7

Match each Azure monitoring service to its function.

Question 8

Match each data transformation concept to its definition.

Question 9

You are designing an Azure Stream Analytics job to process real-time IoT data from thousands of devices. The job must handle late-arriving events (up to 1 hour late) and out-of-order events (up to 5 minutes). Which two temporal policies should you configure?

Accepted Answer

Out of order tolerance window: 5 minutes; Late arrival tolerance window: 1 hour. Azure Stream Analytics uses two distinct temporal policies to handle event timing issues. The 'Late arrival tolerance window' defines how long the system waits for events that arrive after their timestamp (up to 1 hour in this scenario), while the 'Out of order tolerance window' specifies the maximum time difference allowed for events that arrive out of sequence (up to 5 minutes). Option A correctly configures both policies to match the requirements.

Answer

Out of order tolerance window: 1 hour; Late arrival tolerance window: 5 minutes

Answer

Use Event Hubs capture to handle late events; no additional configuration needed

Question 10

A company uses Azure Synapse Analytics dedicated SQL pool for data warehousing. They notice that queries against a large fact table are slow. The table is hash-distributed on ProductID, but many queries filter on OrderDate. What should the data engineer do to improve query performance?

Accepted Answer

Change the distribution to hash on OrderDate. Option D is correct because changing the distribution key to OrderDate aligns the physical data layout with the most common query filter predicate. In a dedicated SQL pool, hash distribution distributes rows across distributions based on the hash of the distribution column. When queries filter on OrderDate, a hash on OrderDate enables partition elimination and distribution-level pruning, reducing data movement and improving scan performance.

Answer

Change the distribution to round-robin

Answer

Replicate the table to all distributions

Answer

Create a columnstore index on OrderDate