DP-203 Design and implement data storage Practice Test 4 — 15 Questions

Question 1

You are designing a data storage solution for a retail company that needs to store transaction data that is frequently updated and requires strong consistency. The solution must support complex queries and joins across multiple tables. Which Azure data service should you recommend?

Accepted Answer

Azure SQL Database. Azure SQL Database is a fully managed relational database service that provides strong consistency, supports complex queries and joins across multiple tables, and is optimized for frequently updated transaction data. It offers ACID compliance and built-in high availability, making it the ideal choice for this retail scenario.

Answer

Azure Cosmos DB

Answer

Azure Synapse Analytics

Answer

Azure Table Storage

Question 2

A multinational corporation is designing a data lake on Azure Data Lake Storage Gen2. The data must be accessible from multiple regions with low latency, but only one region needs writable access. The solution must also comply with data residency requirements. Which two features or configurations should be implemented? (Choose two.)

Accepted Answer

Use read-access geo-redundant storage (RA-GRS). B is correct because read-access geo-redundant storage (RA-GRS) provides a secondary read-only endpoint in a paired region, enabling low-latency reads from multiple regions while maintaining a single writable primary region. This meets the data residency requirement because the secondary region is within the same geography, and RA-GRS ensures data is replicated asynchronously without allowing writes to the secondary.

Answer

Enable hierarchical namespace on the storage account

Answer

Configure geo-replication with manual failover

Answer

Use Azure Data Lake Storage Gen2 with locally redundant storage (LRS)

Question 3

Drag and drop the steps to configure Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage Gen2 into the correct order.

Question 4

Drag and drop the steps to configure Azure Databricks auto-scaling cluster for ETL workloads into the correct order.

Question 5

Drag and drop the steps to implement Azure Data Lake Storage Gen2 lifecycle management to move data to cool and archive tiers into the correct order.

Question 6

Match each Azure service to its primary purpose in a data engineering pipeline.

Question 7

Match each Azure security feature to its description.

Question 8

Match each data storage format to its characteristic.

Question 9

A company wants to ingest streaming data from IoT devices into Azure for real-time analytics. The data must be available for immediate querying and also stored long-term in a cost-effective format. Which Azure service should be used as the primary ingestion endpoint?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct primary ingestion endpoint for streaming IoT data because it is a fully managed, real-time data ingestion service optimized for high-throughput, low-latency event streaming. It can ingest millions of events per second from IoT devices and integrates natively with Azure Stream Analytics and other analytics services for immediate querying, while also supporting long-term retention via Event Hubs Capture to cost-effective storage like Azure Blob Storage or Data Lake Storage.

Answer

Azure SQL Database

Answer

Azure Data Lake Storage Gen2

Answer

Azure Blob Storage

Question 10

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 11

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

Question 12

Which TWO Azure services can be used to implement a data lake architecture for storing structured, semi-structured, and unstructured data?

Accepted Answer

Azure Blob Storage with hierarchical namespace. Azure Data Lake Storage Gen2 (ADLS Gen2) is built on top of Azure Blob Storage and adds a hierarchical namespace, enabling a true data lake architecture. It supports storing structured, semi-structured, and unstructured data at scale, with POSIX-like access control lists and directory-level operations. Azure Blob Storage with hierarchical namespace enabled is essentially the same service as ADLS Gen2, providing the same data lake capabilities.

Answer

Azure SQL Database

Answer

Azure Synapse Analytics

Answer

Azure Cosmos DB

Question 13

Which THREE factors should be considered when designing a partitioning strategy for a large fact table in Azure Synapse Analytics dedicated SQL pool?

Accepted Answer

Data compression. Data compression is a key factor in partitioning strategy because it directly impacts storage efficiency and query performance. In Azure Synapse Analytics dedicated SQL pool, columnstore compression is applied per partition, and choosing the right compression level (e.g., COLUMNSTORE vs. COLUMNSTORE_ARCHIVE) can significantly reduce I/O and improve scan speeds for large fact tables.

Answer

Index type

Answer

Distribution key

Question 14

A data engineer needs to store semi-structured JSON logs for analysis using Azure Synapse Serverless SQL. Which file format should be used for optimal query performance?

Accepted Answer

Parquet. Parquet is correct because it is a columnar storage format that enables predicate pushdown and compression, significantly reducing the amount of data scanned by Azure Synapse Serverless SQL for analytical queries on semi-structured JSON logs. This format aligns with the engine's design for high-performance read operations on large datasets, unlike row-oriented formats that require full file scans.

Answer

Avro

Answer

CSV

Answer

JSON

Question 15

A company is migrating its on-premises SQL Server data warehouse to Azure Synapse Analytics. They have a fact table with 2 billion rows and 30 columns. The table is frequently joined on CustomerID and filtered on OrderDate. What is the recommended table design?

Accepted Answer

Hash-distribute on CustomerID and partition on OrderDate. Hash-distributing the fact table on CustomerID ensures that rows with the same CustomerID are co-located on the same distribution node, which makes joins on CustomerID efficient by avoiding data movement. Partitioning on OrderDate enables partition elimination when filtering by date, reducing the amount of data scanned. This combination optimizes both the join and filter operations for a large fact table in Azure Synapse Analytics.

Answer

Replicate the table to all nodes

Answer

Round-robin distribution with partitions on OrderDate

Answer

Hash-distribute on OrderDate and partition on CustomerID