DP-203 Design and implement data storage Practice Test 11 — 15 Questions

Question 1

You are designing a data lake architecture for a healthcare company. The solution must support fine-grained access control at the file level, encryption at rest and in transit, and integration with Microsoft Purview for data lineage. Which storage solution should you recommend?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Azure Data Lake Storage Gen2 (ADLS Gen2).. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with POSIX-like access control lists (ACLs) for fine-grained file-level permissions, supports encryption at rest (Azure Storage Service Encryption) and in transit (TLS 1.2+), and natively integrates with Microsoft Purview for automated data lineage and cataloging. This makes it ideal for healthcare scenarios requiring strict compliance and auditability.

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Azure NetApp Files.

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Azure Files.

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Azure Blob Storage.

Question 2

A company uses Azure Synapse Analytics dedicated SQL pool. They notice that some queries are slow due to high data movement. What should you do to minimize data movement for queries that join large fact tables?

Accepted Answer

Hash-distribute the fact tables on the join keys.. Hash-distributing the fact tables on the join keys ensures that rows with the same join key value are placed on the same distribution node. This eliminates the need to shuffle data across nodes during the join, minimizing data movement and improving query performance in Azure Synapse dedicated SQL pool.

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Use round-robin distribution for all tables.

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Partition both tables on the join keys.

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Use replicated tables for all large fact tables.

Question 3

You need to store historical sales data for 10 years with infrequent queries. The storage cost must be minimized while retaining the ability to query using Azure Synapse serverless SQL pool. Which storage tier should you use?

Accepted Answer

Azure Storage Cool tier.. The Cool tier is the correct choice because it provides low-cost storage for data that is infrequently accessed (e.g., historical sales data spanning 10 years) while still supporting immediate read access via Azure Synapse serverless SQL pool. Unlike the Archive tier, Cool tier data is online and can be queried without the need for time-consuming rehydration, making it suitable for infrequent but on-demand analytical queries.

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Azure Storage Archive tier.

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Azure Storage Premium tier.

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Azure Storage Hot tier.

Question 4

A financial services company needs to store transaction data for audit purposes. The data must be immutable and cannot be modified or deleted for 7 years. Which Azure storage feature should be used?

Accepted Answer

Azure Blob Storage immutability policy (time-based retention).. Azure Blob Storage immutability policy with time-based retention ensures that blobs cannot be modified or deleted for a specified retention period (e.g., 7 years). This meets the audit requirement for immutable storage by locking the data at the storage level, preventing any writes or deletes until the retention interval expires. The policy is enforced at the container level and applies to all blobs within, making it the correct choice for regulatory compliance.

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Azure Blob Storage soft delete.

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Azure Blob Storage versioning.

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Azure Files share snapshots.

Question 5

Which TWO actions should you take to optimize query performance in Azure Synapse Analytics dedicated SQL pool when working with large fact tables?

Accepted Answer

Create statistics on columns used in WHERE clauses.. Creating statistics on columns used in WHERE clauses enables the Azure Synapse Analytics dedicated SQL pool query optimizer to generate more accurate cardinality estimates, leading to better join strategies and index selections. Without up-to-date statistics, the optimizer may choose suboptimal plans, especially for large fact tables where data distribution skew is common.

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Use replicated distribution for the fact table.

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Use round-robin distribution to evenly distribute data.

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Use clustered index instead of columnstore index.

Question 6

Which THREE components are required to implement a modern data warehouse architecture on Microsoft Azure using Azure Synapse Analytics?

Accepted Answer

Microsoft Purview for data governance and lineage.. Microsoft Purview is required for data governance and lineage in a modern data warehouse architecture on Azure because it provides a unified data governance service that helps manage and monitor data across on-premises, multi-cloud, and SaaS sources. It enables automated data discovery, classification, and lineage tracking, which are essential for compliance and data quality in a modern analytics environment.

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Power BI as the data visualization layer.

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Azure Analysis Services for semantic modeling.

Question 7

Which TWO Azure services can be used to ingest streaming data into Azure Synapse Analytics?

Accepted Answer

Azure Stream Analytics.. Azure Stream Analytics is correct because it is a fully managed stream processing engine that can ingest streaming data from sources like Azure Event Hubs and output directly to Azure Synapse Analytics (via SQL pool or dedicated SQL pool). It enables real-time analytics on streaming data before landing it in Synapse for further analysis.

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Azure Databricks Auto Loader.

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Azure Data Factory.

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Azure Logic Apps.

Question 8

You are reviewing an ARM template snippet for an Azure Blob Storage container. What is the effect of this configuration?

Accepted Answer

It enforces a 7-year immutable retention policy allowing appends.. The ARM template snippet configures a time-based retention policy on the container with a retention period of 2555 days (7 years) and sets `allowProtectedAppendWrites` to true. This enables an immutable storage policy that prevents deletion or modification of existing blobs but allows new append blocks to be added to append blobs, making option C correct.

Answer

It enables legal hold on the container for 2555 days.

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It blocks append writes to blobs in the container.

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It sets a 3-year retention policy blocking any modifications.

Question 9

You are examining a T-SQL script that creates an external table in Azure Synapse serverless SQL pool. The query SELECT * FROM dbo.Sales returns zero rows, but the folder /year=2024/ in ADLS Gen2 contains Parquet files. What is the most likely cause?

Accepted Answer

The credential used to access ADLS Gen2 does not have sufficient permissions.. The most common reason for SELECT * FROM an external table returning zero rows despite data existing in the underlying ADLS Gen2 folder is that the serverless SQL pool lacks the necessary permissions to read the Parquet files. The credential used in the external data source must have at least 'Storage Blob Data Reader' role on the storage account or the container, and the identity (e.g., SAS token, service principal, or managed identity) must be correctly configured. Without this, the query executes successfully but returns no rows because the pool cannot access the data.

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The serverless SQL pool does not support reading Parquet files.

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The external table definition is missing the SCHEMA_NAME parameter.

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The DATA_COMPRESSION setting is incompatible with Parquet files.

Question 10

You are a data engineer for a large e-commerce company. The company uses Azure Synapse Analytics dedicated SQL pool as its enterprise data warehouse. A new business requirement mandates that the Sales fact table, which contains 2 billion rows, must support real-time analytics with a maximum query latency of 1 second for aggregations on the most recent 24 hours of data. The table is currently hash-distributed on CustomerID and partitioned monthly by SaleDate. The current query performance for recent data is slow due to full partition scans. The data is ingested via Azure Event Hubs and processed by Azure Stream Analytics, which writes to staging tables every minute. You need to redesign the storage to meet the latency requirement while minimizing cost and maintaining data integrity. Which approach should you take?

Accepted Answer

Create a separate staging table for the last 24 hours of data with round-robin distribution and a clustered columnstore index. After each batch load, merge the staging table into the main partitioned Sales table. Queries for recent data should target the staging table.. It isolates the hot (recent 24-hour) data into a separate staging table with a clustered columnstore index, which is optimized for fast aggregations and high compression. This avoids full partition scans on the 2-billion-row main table, and merging into the partitioned table after each batch load maintains data integrity without requiring expensive repartitioning or additional dedicated SQL pools.

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Increase partition granularity to hourly partitions for the Sales table.

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Change the Sales table to use a clustered index on SaleDate and a round-robin distribution.

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Provision a second dedicated SQL pool optimized for real-time queries and replicate the recent data there.

Question 11

A company is designing a data lake in Azure Data Lake Storage Gen2 (ADLS Gen2) to store IoT sensor data from millions of devices. The data is ingested in Parquet format, partitioned by date and device ID. The analytics team frequently queries the last 30 days of data for specific device types. Which partition strategy minimizes query cost and optimizes performance?

Accepted Answer

Partition by date (yyyy/MM/dd) first, then by device type (e.g., sensor_type=temp).. Partitioning by date first enables efficient partition pruning for the common query pattern (last 30 days), and then by device type further filters the data within those date partitions. In ADLS Gen2, queries using partition elimination skip entire directories, reducing the amount of data scanned and minimizing query cost. This strategy aligns with the typical query workload, where date-range filtering is the most selective predicate.

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Partition by device ID first, then by date.

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Partition by device ID only, with a separate directory for each device.

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Partition by device type only, with a directory for each type.

Question 12

You are designing a solution to store semi-structured JSON logs from a web application in Azure Cosmos DB. The logs are written once and rarely read. The application writes up to 10,000 documents per second, and each document is about 2 KB. You need to minimize RU/s cost. Which API and indexing policy should you choose?

Accepted Answer

Azure Cosmos DB for MongoDB with a custom indexing policy that only includes a wildcard index on _id and disables automatic indexing.. Azure Cosmos DB for MongoDB with a custom indexing policy that disables automatic indexing and only includes a wildcard index on _id minimizes RU/s cost for write-heavy, rarely read workloads. Each write operation consumes RUs proportional to the number of indexed paths; by eliminating automatic indexing of all fields, you drastically reduce the per-document write RU charge. The MongoDB API supports this fine-grained indexing control, making it ideal for high-throughput ingestion of semi-structured JSON logs.

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Azure Cosmos DB for Cassandra with default indexing policy.

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Azure Cosmos DB for Table with default indexing policy.

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Azure Cosmos DB for NoSQL with default indexing policy (automatic indexing of all fields).

Question 13

Your company uses Azure Synapse Analytics dedicated SQL pool to store a fact table with 2 billion rows. You need to improve query performance for a workload that frequently aggregates sales by date and product category. Which distribution and index type should you use?

Accepted Answer

Hash-distribute on product_category and use a clustered columnstore index.. Hash-distributing on product_category ensures that rows with the same product category are co-located on the same distribution, enabling local aggregation without data movement. A clustered columnstore index provides high compression and batch-mode processing, which is ideal for large fact tables and analytical workloads that aggregate millions of rows by columns like date and product_category.

Answer

Replicate the table and use a clustered index.

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Round-robin distribution and a clustered columnstore index.

Answer

Hash-distribute on date and use a clustered index.

Question 14

You are designing a change data capture (CDC) pipeline to ingest incremental changes from an on-premises SQL Server database into Azure Data Lake Storage Gen2. The pipeline must run every 5 minutes and handle high-volume DML changes. Which Azure service should you use to capture the changes with low latency?

Accepted Answer

Azure Data Factory with a change data capture (CDC) source in the mapping data flow.. Azure Data Factory's mapping data flow includes a native CDC source that can connect to SQL Server and capture incremental DML changes (inserts, updates, deletes) with low latency. This approach uses change tracking or change data capture features in SQL Server to identify changes, and the pipeline can run every 5 minutes to meet the high-volume requirement without custom coding.

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Azure Data Share to share the SQL Server data and capture changes.

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Azure Synapse Pipelines with a copy activity that uses a query to capture changes.

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Azure Databricks with Auto Loader and Delta Live Tables to capture changes.

Question 15

You have an Azure Synapse Analytics dedicated SQL pool with a large fact table partitioned by month. You notice that queries filtering on a specific month still scan all partitions. The table has a clustered columnstore index. What is the most likely cause?

Accepted Answer

The columnstore index has a large number of open rowgroups that are not compressed.. When a clustered columnstore index has a large number of open rowgroups (i.e., rowgroups that have not yet been compressed into columnstore segments), queries may fall back to the delta store, which stores data in a B-tree-like structure. This bypasses partition elimination because the delta store does not maintain the same metadata for partition pruning, causing all partitions to be scanned. Compressing open rowgroups into closed, compressed rowgroups restores proper partition elimination behavior.

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The table is hash-distributed on a column other than date, preventing partition elimination.

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The statistics on the date column are outdated or missing.

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The table is replicated, so partition elimination does not apply.