DP-203 Practice Test 47 — 15 Questions

Question 1

Your team is building a real-time dashboard using Azure Stream Analytics. The data source is an Azure Event Hub that receives clickstream events. You need to output aggregated data (counts per page per minute) to an Azure SQL Database for reporting. The query must handle late-arriving events and ensure exactly-once semantics. Which Stream Analytics feature should you use?

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

Accepted Answer

Use the Input Order section in the Stream Analytics job configuration to set a late arrival tolerance window.. The Input Order section in Azure Stream Analytics job configuration allows you to set a late arrival tolerance window, which handles late-arriving events by buffering them for a specified duration. This ensures that events arriving after their timestamp are still included in the correct window for aggregation, supporting exactly-once semantics when combined with checkpointing and idempotent output to Azure SQL Database.

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Use a temporal window function with a 'late arrival' policy specified in the query.

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Define a watermark in the query to specify a maximum out-of-order tolerance.

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Set the event ordering policy to 'Adjust' to reorder events within a certain time window.

Question 2

Which THREE considerations should be evaluated when designing a partitioning strategy for a large fact table in Azure Synapse Dedicated SQL Pool?

Accepted Answer

Align partition boundaries with distribution key. Aligning partition boundaries with the distribution key in Azure Synapse Dedicated SQL Pool ensures that data movement during partition switching or merging is minimized, as each distribution contains its own set of partitions. This alignment avoids cross-distribution data transfers, which can significantly improve performance and maintainability of large fact tables.

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Use hash distribution for all fact tables

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Choose a partition column with high cardinality

Question 3

A healthcare organization stores patient data in Azure SQL Database. They need to encrypt sensitive columns (e.g., SSN) such that only authorized users can decrypt the data at query time. Which feature should they use?

Accepted Answer

Always Encrypted (with secure enclaves). Always Encrypted with secure enclaves is the correct choice because it allows sensitive columns (e.g., SSN) to be encrypted at rest and in memory, with decryption occurring only on the client side using keys that are never exposed to the database engine. This ensures that only authorized users with the proper encryption keys can decrypt the data at query time, meeting the requirement for column-level encryption with client-side key management.

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Transparent Data Encryption (TDE)

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Azure Information Protection labels

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Dynamic Data Masking

Question 4

Your team is migrating an on-premises SQL Server data warehouse to Azure Synapse Analytics. The source has a fact table with 500 million rows and several dimension tables. You need to choose the best distribution strategy for the fact table to minimize data movement during joins. Which distribution type should you use?

Accepted Answer

Hash distribution on the foreign key column used in joins. Hash distribution on the foreign key column used in joins ensures that rows with the same join key are co-located on the same distribution node. This minimizes data movement because the join can be performed locally on each node without shuffling data across the compute nodes, which is critical for a 500-million-row fact table.

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No distribution (single distribution)

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Replicated distribution

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Round-robin distribution

Question 5

You are designing a data storage solution for a real-time analytics application that ingests IoT sensor data. The data must be stored in a format that supports both streaming ingestion and batch processing with low latency for queries. Which Azure storage solution should you use?

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, enabling both streaming ingestion (via Event Hubs, Kafka, or Spark Structured Streaming) and batch processing (via PolyBase, Azure Synapse, or Databricks) while supporting low-latency queries through its POSIX-like file system and optimized columnar formats like Parquet. It directly addresses the requirement for a unified storage layer that handles real-time and batch workloads without data movement.

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Azure Blob Storage with hot access tier

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Azure SQL Database with change data capture

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Azure Cosmos DB with analytical store

Question 6

A healthcare company stores patient records in Azure Blob Storage. The compliance team requires that all data be encrypted at rest using customer-managed keys (CMK) stored in Azure Key Vault. Additionally, the storage account must be accessible only from a specific virtual network (VNet) and must support versioning to protect against accidental deletion. The storage account is currently using Microsoft-managed keys and has public network access enabled. You need to implement the required changes with minimal downtime. Which course of action should you take?

Accepted Answer

Create a new storage account in the same region with infrastructure encryption enabled and customer-managed keys configured. Enable versioning and VNet access. Use AzCopy to copy data from the old account to the new one. Update applications to use the new storage account. Delete the old account.. Creating a new storage account with infrastructure encryption and customer-managed keys (CMK) is the only way to enable infrastructure encryption, which cannot be enabled on an existing account. Using AzCopy ensures minimal downtime by copying data in the background while applications continue to use the old account until the cutover. Enabling versioning and VNet access on the new account meets all compliance requirements without disrupting the existing environment.

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Modify the existing storage account to use customer-managed keys by updating the encryption settings in the Azure portal. Then, enable versioning and configure firewall rules to allow access from the VNet.

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Delete the existing storage account and recreate it with the same name, enabling customer-managed keys, versioning, and VNet access. Restore data from backups.

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Create a new storage account with the same name in a different region. Enable infrastructure encryption and customer-managed keys. Use Azure Data Factory to copy data from the old account to the new one. Then, delete the old account and update applications to point to the new account.

Question 7

You need to design a storage solution for IoT device telemetry data that will be queried by time range. The data is append-only and arrives at high velocity. Which TWO features should you use to optimize query performance and reduce costs?

Accepted Answer

Store data in columnar format (e.g., Parquet). Columnar formats like Parquet store data by column rather than by row, which significantly reduces I/O when querying only a subset of columns (common in time-range queries). This compression and column pruning directly lowers storage costs and speeds up scan-heavy analytical queries on append-only IoT telemetry data.

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Create indexes on all columns

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Enable row-level security

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Enable geo-redundant storage

Question 8

You are designing a data storage solution for a real-time dashboard that displays streaming data from Azure Event Hubs. The data must be stored in a format that supports both real-time and batch analytics with minimal latency. Which TWO technologies should you use?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is correct because it provides real-time stream processing with low latency, directly ingesting data from Event Hubs and outputting to storage or analytics services. It enables both real-time dashboard queries and batch analytics by writing to a staging store like Azure Data Lake Storage, which can then be queried by Azure Synapse Analytics for historical analysis.

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Azure Data Factory

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Azure Analysis Services

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Azure SQL Database

Question 9

You are designing a data storage solution for a retail company that expects high volumes of small, time-series sensor data from thousands of IoT devices. The data must be stored cost-effectively and queried by time range with low latency. Which Azure data store should you recommend?

Accepted Answer

Azure Data Explorer (ADX). Azure Data Explorer (ADX) is optimized for high-volume, time-series data ingestion and low-latency queries over time ranges. It uses a columnar storage engine and automatic indexing, making it cost-effective for sensor data from thousands of IoT devices.

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Azure Cosmos DB with analytical store

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Azure SQL Database with columnstore indexes

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Azure Blob Storage with Azure Data Lake Storage Gen2

Question 10

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.

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Index type

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Distribution key

Question 11

You are designing a data storage solution for a retail company that needs to store semi-structured IoT sensor data from thousands of devices. The data is ingested in near real-time, and queries will involve filtering by device ID and timestamp. The solution must minimize storage costs while supporting interactive queries. Which TWO Azure data storage options are most appropriate?

Accepted Answer

Azure Blob Storage. Azure Blob Storage (C) is correct because it provides a cost-effective, scalable object store for semi-structured IoT data, supporting near real-time ingestion via REST APIs or SDKs and enabling interactive queries through Azure Data Lake Storage Gen2's hierarchical namespace and integration with query engines like Azure Synapse Serverless SQL or PolyBase. Azure Data Lake Storage Gen2 (D) is correct as it builds on Blob Storage with a hierarchical namespace, optimized for analytics workloads and interactive queries using tools like Azure Synapse or Databricks, while minimizing costs through tiered storage and lifecycle management.

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Azure Table Storage

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Azure Cosmos DB

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Azure SQL Database

Question 12

You are designing a solution to store large amounts of log data that is written once and accessed rarely. The data must be retained for 7 years for compliance. After 30 days, the data should be moved to a lower-cost storage tier. After 1 year, the data should be archived. Which Azure Storage lifecycle management policy should you implement for an Azure Data Lake Storage Gen2 account?

Accepted Answer

Transition to cool tier after 30 days; transition to archive tier after 365 days; delete after 2555 days (7 years).. It aligns with the specified lifecycle requirements: transition to cool tier after 30 days for cost savings, transition to archive tier after 365 days for long-term retention, and delete after 2555 days (7 years) for compliance. Azure Data Lake Storage Gen2 supports lifecycle management policies that automate tier transitions and deletion based on age, ensuring data is moved to lower-cost storage as access patterns change.

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Transition to cool tier after 30 days; delete after 7 years.

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Transition to archive tier after 30 days; delete after 7 years.

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Transition to cool tier after 30 days; transition to cool tier again after 365 days.

Question 13

A company uses Azure Synapse Analytics with a dedicated SQL pool. They need to ensure that a team of data scientists can query all tables in the 'sales' schema but cannot modify any data or schema objects. Which role should the team be assigned?

Accepted Answer

db_datareader. The `db_datareader` role grants read-only access to all user tables in a database, allowing the team to query all tables in the 'sales' schema without the ability to modify data or schema objects. This aligns perfectly with the requirement for data scientists to perform SELECT queries only.

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db_owner

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db_ddladmin

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db_datawriter

Question 14

You have a Synapse Analytics dedicated SQL pool. You need to load 100 GB of CSV data from Azure Data Lake Storage Gen2 into a fact table. The table has a hash-distributed column. Which pattern is most efficient for loading with minimal impact on concurrent queries?

Accepted Answer

Use CREATE TABLE AS SELECT (CTAS) with the hash-distributed column. CTAS with a hash-distributed column loads data directly into the target table with the same distribution scheme, avoiding data movement and minimizing resource contention. This pattern is optimized for bulk loading large datasets into a hash-distributed fact table, as it leverages the Synapse SQL pool's MPP architecture to parallelize the operation without blocking concurrent queries.

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Use PolyBase INSERT...SELECT with rowstore table

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Use COPY INTO command with a round-robin distribution

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Use Azure Data Factory Copy activity with staging enabled

Question 15

Your company is building a real-time analytics solution for monitoring manufacturing equipment. Sensors send JSON data every second to an Azure Event Hubs instance. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and hour. You use Azure Stream Analytics to read from Event Hubs and write to ADLS Gen2. Currently, the output is writing many small Parquet files (under 1 MB each), which is causing performance issues when reading the data. You need to optimize the output to produce fewer, larger files while maintaining low latency. What should you do?

Accepted Answer

Increase the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2. Increasing the 'Maximum events per batch' setting in the Stream Analytics output to ADLS Gen2 allows more events to be accumulated before writing a file, resulting in fewer, larger Parquet files. This directly addresses the small-file problem while maintaining low latency, as the batching is time-bound and does not introduce excessive delay.

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Partition the output by minute instead of hour to distribute data more

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Change the output format to Avro to improve compression

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Decrease the 'Maximum events per batch' setting to reduce latency