DP-203 Practice Test 49 — 15 Questions

Question 1

You need to design a data storage solution for a batch processing pipeline that processes petabytes of data daily. The data is stored in Parquet format and must be accessible by both Azure Databricks and Azure Synapse Analytics. Which storage solution should you recommend?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing native POSIX-like access control and high throughput for petabyte-scale batch processing. Both Azure Databricks and Azure Synapse Analytics have optimized connectors for ADLS Gen2 that leverage the hierarchical namespace for efficient partition pruning and file listing, which is critical for Parquet-based analytics at this scale.

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Azure Files

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Azure SQL Database

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Azure Blob Storage

Question 2

A multinational bank needs to store customer transaction records for 10 years to meet regulatory compliance. The data is rarely accessed after the first year. The solution must minimize storage costs while allowing queries on recent data with low latency. Which tiering strategy should you implement?

Accepted Answer

Use Azure Blob Storage with lifecycle management to transition from Hot to Cool to Archive tiers. Option D is correct because Azure Blob Storage lifecycle management automatically transitions blobs from Hot to Cool to Archive tiers based on age, minimizing storage costs for rarely accessed data after the first year while keeping recent data in Hot tier for low-latency queries. This aligns with the 10-year retention requirement and cost optimization goal without manual intervention.

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Store all data in Azure SQL Database with partitioning and drop older partitions

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Use Azure Data Lake Storage Gen2 with a single storage tier

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Store data in Azure Cosmos DB with time-to-live (TTL) and use Azure Blob Storage for backups

Question 3

A data engineer needs to store log data from multiple applications in Azure. The data is append-only, heavily compressed, and queried infrequently. Cost minimization is critical. Which storage solution is best?

Accepted Answer

Azure Blob Storage with cool or archive access tier. Azure Blob Storage with cool or archive access tier is the best choice because the data is append-only, heavily compressed, and infrequently queried, making cost minimization the top priority. The cool tier offers low storage costs with higher access charges, while the archive tier provides the lowest storage cost for data that is rarely accessed and can tolerate hours of retrieval latency. This aligns perfectly with the append-only, infrequently queried nature of the log data.

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Azure Table Storage

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Azure Cosmos DB with analytical store

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Azure Data Lake Storage Gen2 with hot tier

Question 4

You are designing a data pipeline to ingest streaming data from IoT devices into Azure Synapse Analytics. The data must be available for querying with minimal latency, but you also need to handle spikes in throughput without data loss. Which service should you use as the ingestion layer?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice because it is a fully managed, real-time data ingestion service optimized for high-throughput streaming data from millions of IoT devices. It provides at-least-once delivery, supports partitioning for massive scale, and integrates natively with Azure Synapse Analytics via the Synapse Pipeline or Event Hubs Capture to handle throughput spikes without data loss.

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Azure Data Lake Storage Gen2

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Azure Blob Storage

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Azure IoT Hub

Question 5

You are using Azure Synapse Analytics to process streaming data from Azure Event Hubs. The data must be written to a Delta Lake table in ADLS Gen2 with exactly-once semantics. Which processing engine should you use?

Accepted Answer

Azure Synapse Pipeline with Mapping Data Flow. Option C is correct because Azure Synapse Pipeline with Mapping Data Flow supports Delta Lake as a sink and can be configured to use Spark-based execution for streaming data from Event Hubs, enabling exactly-once semantics through checkpointing and idempotent writes. Mapping Data Flow runs on Spark clusters within Synapse, providing the necessary transactional guarantees for Delta Lake tables.

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Azure Databricks with Structured Streaming

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Azure Synapse serverless SQL pool

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Azure Stream Analytics

Question 6

You are developing a data processing solution in Azure Synapse Analytics. The solution must use a serverless SQL pool to query Parquet files stored in Azure Data Lake Storage Gen2. Which authentication method should you use to ensure that the queries use the identity of the caller and adhere to Azure role-based access control (RBAC) permissions?

Accepted Answer

Microsoft Entra ID pass-through authentication.. Microsoft Entra ID pass-through authentication (option A) is correct because it allows the serverless SQL pool to use the caller's identity when accessing Azure Data Lake Storage Gen2. This ensures that Azure RBAC permissions (e.g., Storage Blob Data Reader) assigned to the user are evaluated for each query, providing fine-grained access control without exposing storage account keys or tokens.

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Storage account key.

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Shared access signature (SAS) token.

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Service principal with a secret.

Question 7

You have an Azure Data Factory pipeline that loads data from an on-premises SQL Server to an Azure Synapse Analytics dedicated SQL pool. The pipeline uses a staging Azure Blob Storage account. Recently, the pipeline has been failing with timeout errors. You need to ensure the pipeline completes successfully within the scheduled window. What should you do?

Accepted Answer

Increase the staging blob's copy timeout and enable parallel copy in the copy activity. The timeout errors occur because the default copy timeout for staging blob storage is insufficient for large data volumes or slow network conditions. Increasing the staging blob's copy timeout and enabling parallel copy in the copy activity directly addresses this by allowing more time for data transfer and leveraging multiple concurrent connections to improve throughput, ensuring the pipeline completes within the scheduled window.

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Move the staging storage to Azure Data Lake Storage Gen2

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Increase the dedicated SQL pool's data warehouse units (DWU)

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Use PolyBase instead of staging for data loading

Question 8

You are designing a data lake for a manufacturing company that will store sensor readings in Parquet format. The data will be used by data scientists for batch training and by analysts for ad-hoc queries. Which Azure service should you use as the primary storage layer?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct primary storage layer because it combines a hierarchical namespace with Azure Blob Storage's scalable object storage, providing POSIX-compliant access control and directory-level operations. This makes it ideal for storing Parquet files used by data scientists for batch training (via Spark or PolyBase) and by analysts for ad-hoc queries (via Synapse SQL or Azure Databricks), as it supports both high-throughput analytics and fine-grained security.

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Azure Synapse SQL Pool

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Azure Blob Storage

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Azure SQL Database

Question 9

A data engineer needs to store semi-structured JSON logs from IoT devices. The data will be queried using SQL and must support high-throughput writes. Which Azure data store is most appropriate?

Accepted Answer

Azure Cosmos DB with Core (SQL) API. Azure Cosmos DB with Core (SQL) API is the most appropriate choice because it natively stores semi-structured JSON documents, supports high-throughput writes with single-digit millisecond latency, and allows querying the JSON data directly using SQL syntax. Its schema-agnostic design and automatic indexing make it ideal for IoT workloads where device telemetry arrives at high velocity and must be immediately queryable.

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Azure Blob Storage with JSON blobs

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Azure Data Lake Storage Gen2 with JSON files and PolyBase

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Azure SQL Database with JSON columns

Question 10

A data engineering team uses Azure Data Factory to load data from Azure SQL Database to Azure Data Lake Storage Gen2. They notice that the pipeline runs fail intermittently due to transient errors. They need to implement a retry policy with exponential backoff. What is the most efficient way to achieve this?

Accepted Answer

Configure the 'Retry' property on the copy activity with a count and exponential backoff interval. Option D is correct because Azure Data Factory natively supports configuring a 'Retry' property on activities, including Copy activities, with an exponential backoff interval. This built-in mechanism automatically retries the activity upon transient failures without requiring custom logic, making it the most efficient and maintainable approach for handling intermittent errors.

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Use a 'Validation' activity before the copy to check source availability

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Create a custom .NET activity to handle retries

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Add a 'Until' loop with a wait activity in the pipeline

Question 11

Your company stores sensitive customer data in Azure Data Lake Storage Gen2. You need to implement a security solution that prevents unauthorized access from the public internet while allowing access from specific Azure services and on-premises networks. Which feature should you configure?

Accepted Answer

Configure firewall rules and virtual network service endpoints.. Option C is correct because configuring firewall rules and virtual network service endpoints allows you to restrict access to your Azure Data Lake Storage Gen2 account to only traffic originating from specific virtual networks and trusted Azure services, while blocking all public internet traffic. This provides a network-level security boundary that meets the requirement of preventing unauthorized public access while permitting access from on-premises networks (via VPN/ExpressRoute) and specific Azure services.

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Use private endpoints for all storage accounts.

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Enable Microsoft Entra ID authentication only.

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Use shared access signatures (SAS) with IP restrictions.

Question 12

You have an Azure Synapse Analytics dedicated SQL pool with a table that uses hash distribution on CustomerID. You notice that queries joining this table with another table on OrderDate are slow. What is the most likely cause?

Accepted Answer

The join columns are not aligned; data must be shuffled across distributions. D is correct because in Azure Synapse Analytics dedicated SQL pools, hash distribution distributes rows across distributions based on a hash of the distribution key (CustomerID). When joining on OrderDate, which is not the distribution key, the join columns are not aligned across distributions. This forces data movement (shuffling) where rows from one or both tables must be redistributed to match the join key, causing significant performance degradation.

Answer

The table is not partitioned by OrderDate

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Statistics on the join columns are outdated

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The table should use round-robin distribution instead

Question 13

You are designing a data lake architecture using Azure Data Lake Storage Gen2. The data will be ingested from multiple sources with varying schemas. You need to organize the data in a way that supports both batch and streaming analytics while maintaining data lineage. Which folder structure convention should you use?

Accepted Answer

Use a medallion architecture with three layers: bronze (raw), silver (cleaned), gold (aggregated).. Option C is correct because the medallion architecture (bronze, silver, gold) is the recommended pattern for Azure Data Lake Storage Gen2 when handling multiple sources with varying schemas. It supports both batch and streaming by storing raw data in bronze, applying incremental transformations in silver, and serving aggregated views in gold, while maintaining data lineage through clear layer boundaries and audit columns.

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Organize by ingestion date only, with subfolders for each source.

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Organize by source system, then by date.

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Organize by file format (CSV, Parquet, JSON) and date.

Question 14

You need to design a storage solution for streaming data from IoT devices. The solution must support real-time analytics and long-term storage for historical analysis. Which combination of Azure services should you use?

Accepted Answer

Azure Event Hubs and Azure Data Lake Storage Gen2. Azure Event Hubs is designed for high-throughput, low-latency ingestion of streaming data from IoT devices, supporting real-time analytics via integration with Azure Stream Analytics. Azure Data Lake Storage Gen2 provides hierarchical namespace and POSIX-compliant access for long-term storage, enabling efficient historical analysis with tools like Azure Synapse Analytics or Spark. This combination meets both real-time and historical requirements without the limitations of other options.

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Azure Queue Storage and Azure Cosmos DB

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Azure Event Hubs and Azure Blob Storage

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Azure IoT Hub and Azure SQL Database

Question 15

Which THREE factors should be considered when choosing between Azure Synapse Dedicated SQL Pool and Azure SQL Database for a data warehouse workload?

Accepted Answer

Concurrency and workload management. Option B is correct because Azure Synapse Dedicated SQL Pool provides built-in workload management features like workload classification, importance, and concurrency slots, which are essential for managing mixed analytical workloads with varying resource demands. Azure SQL Database, while supporting resource governance, lacks the granular workload management capabilities needed for complex data warehouse scenarios with concurrent queries.

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Need for geo-replication

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Integration with Azure Data Lake Storage