DP-203 Practice Test 4 — 25 Questions

Question 1

You are designing a data processing solution in Azure Synapse Analytics that uses serverless SQL pools to query Parquet files in Azure Data Lake Storage Gen2. The files are partitioned by year and month. You need to optimize query performance and reduce data scanned. What should you do?

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

Accepted Answer

Create views that filter on partition columns.. Serverless SQL pools in Azure Synapse Analytics support partition elimination only when queries use views or inline queries that explicitly filter on partition columns (e.g., year, month) in the WHERE clause. This allows the pool to skip scanning irrelevant partitions, reducing data scanned and improving performance. Creating views that encapsulate these filters ensures consistent partition pruning across queries.

Answer

Use CREATE EXTERNAL TABLE AS SELECT (CETAS) to create new external tables.

Answer

Use OPENROWSET with the DATA_SOURCE parameter.

Answer

Increase the number of files per partition.

Question 2

You need to audit all data access to an Azure Storage account. Which Azure service should you enable?

Accepted Answer

Azure Storage analytics logs and send to Log Analytics workspace. Azure Storage analytics logs capture detailed information about successful and failed requests to a storage account, including authentication details, IP addresses, and operation types. By sending these logs to a Log Analytics workspace, you can query and analyze them using KQL, enabling comprehensive auditing of all data access. This is the correct service for auditing because it provides the granular, queryable logs required for security and compliance audits.

Answer

Azure Policy to audit storage account access

Answer

Azure Monitor metrics

Answer

Azure Security Center

Question 3

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. 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.

Answer

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

Answer

Use Azure Data Lake Storage Gen2 with a single storage tier

Answer

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

Question 4

You are designing a data storage solution for real-time analytics on IoT telemetry. The system must ingest 10,000 events per second and support sub-second query latency. Which Azure data store should you use?

Accepted Answer

Azure Data Explorer (ADX).. Azure Data Explorer (ADX) is purpose-built for high-velocity telemetry and log analytics, ingesting 10,000+ events per second with sub-second query latency via its columnar storage and distributed query engine. It supports real-time analytics on streaming IoT data without requiring pre-defined schemas or indexing, making it the optimal choice for this scenario.

Answer

Azure Table Storage.

Answer

Azure SQL Database with in-memory OLTP.

Answer

Azure Cosmos DB with analytical store.

Question 5

You need to store semi-structured JSON data from a web application. The data schema may change over time. The solution must support low-latency queries and be globally distributed. Which Azure data service should you use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is the correct choice because it natively supports semi-structured JSON documents with a flexible schema, offers single-digit-millisecond latency for queries, and provides global distribution with turnkey multi-region replication. Its API for MongoDB or SQL API allows direct ingestion of JSON data, and its schema-agnostic indexing adapts automatically to schema changes over time.

Answer

Azure Table Storage

Answer

Azure Data Lake Storage Gen2

Answer

Azure SQL Database

Question 6

Match each Azure Synapse Analytics component to its function.

Accepted Answer

Dedicated SQL pool: Provisioned compute resources for high-performance querying of relational data.. In Azure Synapse Analytics, Dedicated SQL pool provides provisioned compute for relational data, Serverless SQL pool queries data lake files on-demand, Apache Spark pool handles distributed processing with Spark, and Pipeline orchestrates workflows. Common confusions include mixing serverless and provisioned capabilities or attributing Spark functionality to SQL pools.

Answer

Dedicated SQL pool: Serverless querying of data in data lake.

Answer

Serverless SQL pool: Managed Spark clusters for big data analytics.

Question 7

A manufacturing company uses Azure Data Lake Storage Gen2 to store IoT sensor data. The data arrives in JSON format with a nested structure. You need to transform the data into a tabular format for downstream analytics using Azure Synapse Pipelines. Which data flow transformation should you use?

Accepted Answer

Flatten transformation. The Flatten transformation in mapping data flows unpacks nested arrays into rows. Option A is wrong because the Aggregate transformation groups data but does not flatten nested structures. Option C is wrong because the Window transformation calculates aggregated values over a range of rows. Option D is wrong because the Pivot transformation rotates rows to columns.

Answer

Aggregate transformation

Answer

Window transformation

Answer

Pivot transformation

Question 8

Which TWO options are correct for configuring a copy activity in Azure Data Factory to load data from Azure Blob Storage to Azure SQL Database?

Accepted Answer

Use a staging table in Azure SQL Database before inserting into the final table.. Correct options are A and B. Option A (using a staging table in Azure SQL Database before inserting into the final table) is a best practice for large data loads, enabling efficient truncate-and-reload or upsert operations. Option B (using staging via Azure Blob Storage when loading large volumes) improves performance by batching and parallelizing the load, reducing timeouts and resource contention. Option D is incorrect because PolyBase is not supported for Azure SQL Database; it is available only for Azure Synapse Analytics and SQL Server. Option C is incorrect because Azure Data Lake Storage is not the standard staging location for Azure SQL Database; Azure Blob Storage is used. Option E is incorrect because skipping staging can degrade performance for large data volumes.

Answer

Use Azure Data Lake Storage as the staging location for better throughput.

Answer

Use PolyBase to load directly from Blob Storage to Azure SQL Database.

Answer

Always skip staging to reduce latency.

Question 9

Your organization uses Azure Data Lake Storage Gen2 for a data lake. You need to prevent accidental deletion of data by enabling a soft delete policy. Which configuration is required?

Accepted Answer

Enable blob soft delete on the storage account.. Azure Data Lake Storage Gen2 supports blob soft delete, which protects against accidental deletion by retaining deleted blobs for a specified retention period. Option A is incorrect because Azure Resource Manager locks prevent the deletion or modification of the storage account itself, not the data within it. Option B is incorrect because Azure Backup is designed for backing up VMs, SQL databases, and other workloads, not for managing soft delete of blobs. Option C is incorrect because blob versioning preserves previous versions of blobs, but it does not prevent deletion of the current version; soft delete is specifically for recovery from accidental deletion.

Answer

Apply an Azure Resource Manager lock.

Answer

Configure Azure Backup for the storage account.

Answer

Enable blob versioning.

Question 10

A company uses Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage Gen2. They notice that queries are slow and want to improve performance by reducing the amount of data read. What is the most effective strategy?

Accepted Answer

Partition the data by a frequently filtered column and use file elimination in queries.. Partitioning the data in the lake and using partition elimination reduces data read. Option B is wrong because increasing the number of compute nodes is not possible in serverless; it's auto-scaling. Option C is wrong because OPENROWSET with CSV reads all files; it does not reduce data read. Option D is wrong because CETAS is for creating external tables, not for improving query performance directly.

Answer

Increase the number of compute nodes in the serverless pool.

Answer

Use OPENROWSET with CSV format instead of Parquet.

Answer

Create external tables using CETAS and query them.

Question 11

Your company uses Azure Data Lake Storage Gen2 as the central data lake. You need to process batch data using serverless Spark jobs that can be scheduled daily. Which Azure service should you use?

Accepted Answer

Azure Synapse Analytics serverless Spark pool with pipelines.. Azure Synapse Analytics provides serverless Spark pools with built-in scheduling via pipelines, allowing you to run daily batch jobs without managing clusters. Option A (Azure Batch) is for custom compute workloads, not Spark jobs. Option C (Azure Databricks) requires a job cluster that is not serverless. Option D (Azure Machine Learning) is designed for ML workflows, not general batch data processing.

Answer

Azure Batch with custom Spark containers.

Answer

Azure Databricks with a job cluster.

Answer

Azure Machine Learning with Spark compute.

Question 12

You are using Azure Stream Analytics to process real-time data from an event hub and output to Azure Synapse Analytics. You need to ensure exactly-once delivery semantics to the output. What should you configure?

Accepted Answer

Set the output to 'Exactly Once' delivery mode.. Azure Stream Analytics supports exactly-once delivery to Azure Synapse Analytics by configuring the output to 'Exactly Once' delivery mode. Option A is incorrect because the input consumption mode does not control output delivery guarantees. Option B is incorrect because event ordering and late arrival policies handle timing, not delivery semantics. Option C is incorrect because checkpointing is for managing internal query state, not for ensuring exactly-once output.

Answer

Set the input to 'Exactly Once' consumption mode.

Answer

Configure event ordering and late arrival policies.

Answer

Enable checkpointing in the query.

Question 13

Refer to the exhibit. You are reviewing an ARM template for an Azure Data Lake Storage Gen2 account. Which of the following security best practices is violated in this template?

Accepted Answer

The account allows HTTP traffic and uses an outdated TLS version. The template sets supportsHttpsTrafficOnly to false, allowing HTTP traffic, which is insecure. Additionally, minimumTlsVersion is set to TLS1_0, an outdated and insecure version. Option A is incorrect because the location uses resourceGroup().location, which is a common and acceptable practice for flexibility. Option B is incorrect because Standard_LRS is a valid SKU; Geo-redundant storage is not a security best practice requirement. Option C is incorrect because the template does enable hierarchical namespace (isHnsEnabled: true), so that is not a violation.

Answer

The location should be fixed instead of using resourceGroup().location

Answer

The SKU should be Standard_GRS for disaster recovery

Answer

The account does not enable hierarchical namespace (HNS)

Question 14

You are building a data processing solution using Azure Databricks. The solution must process streaming data from Azure Event Hubs, join it with a static reference table stored in Azure Data Lake Storage Gen2 (Parquet format), and write the output to Azure Synapse Analytics. The reference table is updated daily. Which approach minimizes latency and ensures data consistency?

Accepted Answer

Use Spark Structured Streaming with a streaming join and cache the reference table as a static DataFrame.. Using Spark Structured Streaming with a streaming join and caching the reference table as a static DataFrame minimizes latency and ensures consistency by reading the reference table once and caching it for the duration of the stream. This avoids reloading the reference table in each micro-batch (as in option C) and avoids the latency of batch processing (as in option D). Option B, foreachBatch, is useful for writing to sinks like Synapse but does not address the join performance or consistency of the reference data.

Answer

Use Spark Structured Streaming with foreachBatch to write to Synapse.

Answer

Use Spark Structured Streaming with a streaming join and load the reference table in each micro-batch.

Answer

Use a batch job that runs every hour to process the data.

Question 15

You have an Azure Stream Analytics job that writes output to Azure Synapse Analytics. You need to ensure that the job can authenticate to Synapse Analytics using a managed identity. What should you do?

Accepted Answer

Enable system-assigned managed identity on the Stream Analytics job and configure the output to use it.. To authenticate to Azure Synapse Analytics using a managed identity, you should enable the system-assigned managed identity on the Stream Analytics job (it is enabled by default) and then configure the output to use managed identity authentication. Option A is correct. Option B is incorrect because a SAS token is not a managed identity. Option C is incorrect because while a user-assigned managed identity could be used, the system-assigned managed identity is sufficient and is the simpler option. Option D is incorrect because SQL Server authentication does not use managed identity and is less secure than managed identity authentication.

Answer

Generate a shared access signature (SAS) token for the Synapse Analytics workspace.

Answer

Create a user-assigned managed identity and assign it to the Stream Analytics job.

Answer

Configure the output to use SQL Server authentication with a username and password.

Question 16

A company uses Azure Databricks for data processing. They want to monitor the performance of Spark jobs and set up alerts for job failures. Which Azure service should they use?

Accepted Answer

Azure Monitor. Azure Monitor is the central service for collecting metrics and logs from Azure Databricks, enabling performance monitoring and alerting on Spark jobs. Option A is incorrect because Azure Advisor provides recommendations but not real-time monitoring or alerts. Option B is incorrect because Azure Sentinel is a SIEM solution for security incidents, not for job performance monitoring. Option C is incorrect because Azure Log Analytics is a component of Azure Monitor used for log analysis, but the overarching service for monitoring and alerts is Azure Monitor.

Answer

Azure Advisor

Answer

Azure Sentinel

Answer

Azure Log Analytics

Question 17

Your Azure Data Factory pipeline uses a Copy activity to load data from an on-premises SQL Server to Azure Blob Storage. You notice that the pipeline is running slower than expected. You need to identify the bottleneck. Which Data Factory monitoring metric should you analyze first?

Accepted Answer

Data Integration Unit (DIU) consumption. Data Integration Unit (DIU) consumption indicates whether the Copy activity is resource-bound and helps identify performance bottlenecks. Option A is wrong because source queue length reflects integration runtime queue depth, not a direct metric for Copy activity throughput. Option B is wrong because pipeline duration is a result of performance issues, not a metric to pinpoint the bottleneck first. Option C is wrong because activity run count is unrelated to performance analysis.

Answer

Source queue length

Answer

Pipeline duration

Answer

Activity run count

Question 18

You are tasked with designing a data storage solution for a social media analytics company. They need to store user profile data (JSON) and social media posts (text and images). The data is used for machine learning models that require fast random access to individual user profiles and the ability to run analytical queries over posts. The solution must provide low-latency reads for user profiles (milliseconds) and support for large-scale analytics on posts. Which combination of Azure data services should you recommend?

Accepted Answer

Azure Cosmos DB for user profiles and Azure Data Lake Storage Gen2 for posts. Azure Cosmos DB provides low-latency (millisecond) reads for user profiles via its indexing and partitioning capabilities, ideal for fast random access. Azure Data Lake Storage Gen2 (ADLS Gen2) combines a hierarchical namespace with Blob Storage, enabling large-scale analytical queries on posts using tools like Azure Synapse Analytics or Spark, while efficiently storing text and images.

Answer

Azure Cosmos DB for both user profiles and posts

Answer

Azure SQL Database for both user profiles and posts

Answer

Azure Table Storage for user profiles and Azure Blob Storage for posts

Question 19

You are designing a data pipeline in Azure Synapse Analytics to ingest data from Azure Blob Storage into a dedicated SQL pool. The source files are CSV with varying row lengths, and you need to ensure optimal performance for reads. Which file format and compression should you recommend?

Accepted Answer

Parquet with Snappy compression. Parquet with Snappy compression is optimal for dedicated SQL pools in Azure Synapse Analytics because Parquet is a columnar format that enables efficient predicate pushdown and column pruning, reducing I/O. Snappy provides fast compression/decompression with minimal CPU overhead, which is critical for high-throughput reads in a distributed MPP environment.

Answer

Avro with Deflate compression

Answer

CSV with Gzip compression

Answer

ORC with Zlib compression

Question 20

Which TWO actions should you take to secure data in transit between an Azure Synapse Analytics serverless SQL pool and a client application?

Accepted Answer

Configure the serverless SQL pool to enforce TLS 1.2 connections.. The correct answers are B and C. Enforcing TLS 1.2 ensures encryption in transit using a modern, secure protocol (B). Using Azure Virtual Network service endpoints for the SQL pool keeps traffic within the Azure backbone network, adding a layer of network security (C). Option A (Azure RBAC) controls authentication and authorization, not encryption. Option D (disable SSL) would expose data in transit. Option E (ExpressRoute) provides private connectivity but does not inherently encrypt data; it's more for network isolation.

Answer

Use Azure RBAC to restrict access to the SQL pool.

Answer

Disable SSL encryption to reduce latency.

Answer

Use Azure ExpressRoute to connect to the SQL pool.