DP-900 Describe an analytics workload on Azure Practice Test 12 — 15 Questions

Question 1

A data engineer needs to design a solution for a healthcare organization that must store patient records for 7 years to comply with regulatory requirements. The data will be accessed infrequently after the first year. Which Azure storage tier should be used for data older than one year?

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

Accepted Answer

Cool tier. The Cool tier is designed for data that is accessed infrequently but must be available immediately when needed, with a minimum storage duration of 30 days. Since patient records older than one year are accessed rarely but may still require low-latency retrieval for compliance audits, Cool tier balances cost and accessibility. Hot tier is for frequent access, Archive tier has a retrieval latency of hours (not suitable for occasional access), and Transaction-optimized is not a standard Azure Blob Storage tier.

Answer

Transaction-optimized tier

Answer

Archive tier

Answer

Hot tier

Question 2

A data analyst needs to create a report in Power BI that combines sales data from Azure SQL Database and inventory data from Azure Cosmos DB. The report should refresh daily. Which Power BI feature should be used to combine these data sources?

Accepted Answer

Power Query. Power Query allows connecting to multiple data sources (like Azure SQL Database and Azure Cosmos DB) and combining them through merge or append queries. This is the correct feature for combining data from different sources. Option A (Quick Measures) is for creating quick calculations within a single table, not for combining sources. Option B (DAX) is a formula language used for creating calculated columns or measures, not for data ingestion or combining sources. Option C (Power BI Desktop) is the application itself, not a specific feature for combining data.

Answer

Quick Measures

Answer

Data Analysis Expressions (DAX)

Answer

Power BI Desktop

Question 3

A company uses Azure Data Lake Storage Gen2 to store IoT sensor data. The data is partitioned by date and sensor ID. A data scientist needs to efficiently query only the last 7 days of data for a specific sensor. Which strategy minimizes the amount of data scanned?

Accepted Answer

Use a directory structure that enables partition elimination. Azure Data Lake Storage Gen2 supports hierarchical directory structures that enable partition elimination at the storage layer. By organizing data under a path like `/sensorID=123/date=2025-03-20/`, a query engine (e.g., Azure Synapse Serverless SQL or Spark) can skip entire directories that do not match the filter, drastically reducing the amount of data scanned.

Answer

Create a view that filters on date and sensor ID

Answer

Read all Parquet files and filter using a WHERE clause

Answer

Create an index on the date and sensor ID columns

Question 4

A multinational corporation uses Azure Synapse Analytics serverless SQL pool to query data in Azure Data Lake Storage. The security team requires that access to specific columns containing personally identifiable information (PII) be restricted based on the user's role. Which feature should be implemented?

Accepted Answer

Column-level security. Column-level security (CLS) in Azure Synapse Analytics serverless SQL pool allows you to restrict access to specific columns containing PII based on the user's role or identity. By granting or denying SELECT permissions on individual columns, you can ensure that only authorized users see sensitive data while others see NULL or an error. This directly meets the requirement to restrict column access by role.

Answer

Row-level security (RLS)

Answer

Azure Purview data classification

Answer

Dynamic data masking

Question 5

A company wants to build a near-real-time analytics solution on Azure. IoT devices send telemetry data to Azure Event Hubs. The data must be processed and stored in Azure Cosmos DB for low-latency queries. Which Azure service should be used to process the streaming data?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a fully managed stream processing engine designed specifically for real-time analytics on high-throughput data streams from sources like Azure Event Hubs. It can run SQL-like queries to filter, aggregate, and join streaming data, and output results directly to Azure Cosmos DB for low-latency queries, making it ideal for near-real-time IoT analytics.

Answer

Azure Logic Apps

Answer

Azure Functions

Answer

Azure Data Factory

Question 6

A data analyst uses Power BI to create a report that combines data from Azure Synapse Analytics and an on-premises SQL Server database. The on-premises data must be refreshed every hour. Which component is required to connect to the on-premises data source?

Accepted Answer

On-premises data gateway. The on-premises data gateway is required to securely connect Power BI to on-premises SQL Server databases for scheduled refreshes. It acts as a bridge, transmitting data from the on-premises source to the Power BI service without opening inbound firewall ports. This component is specifically designed for self-service analytics scenarios where cloud services need to access on-premises data sources.

Answer

Azure VPN Gateway

Answer

Azure ExpressRoute

Answer

Azure Data Factory

Question 7

Which TWO Azure services can be used to orchestrate and automate data pipelines? (Choose two.)

Accepted Answer

Azure Synapse Pipelines. Azure Data Factory (E) is a dedicated cloud-based ETL and data integration service that allows you to create, schedule, and orchestrate data pipelines at scale. Azure Synapse Pipelines (B) is built on the same engine as Azure Data Factory and provides native pipeline orchestration within the Synapse Analytics workspace, enabling you to move and transform data across various sources and sinks. Both services offer visual design tools, code-free transformations, and robust scheduling capabilities for automating data workflows.

Answer

Azure SQL Database

Answer

Power BI

Answer

Azure Databricks

Question 8

Which TWO Azure services can be used to store semi-structured data? (Choose two.)

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a NoSQL database service that natively supports semi-structured data through its flexible schema model. It allows storing JSON documents, key-value pairs, and graph data without requiring a fixed schema, making it ideal for semi-structured data like user profiles, IoT telemetry, or product catalogs.

Answer

Azure SQL Database

Answer

Azure Synapse Analytics

Answer

Power BI

Question 9

Your company uses Azure Data Lake Storage Gen2 and wants to grant a data scientist read-only access to a specific container. Which built-in RBAC role should you assign?

Accepted Answer

Storage Blob Data Reader. Storage Blob Data Reader (D) is the correct built-in RBAC role because it grants read-only access to Azure Storage blob containers and data, including Data Lake Storage Gen2. This role provides the necessary permissions for a data scientist to read data from a specific container without allowing write or delete operations.

Answer

Storage Account Contributor

Answer

Reader

Answer

Storage Blob Data Contributor

Question 10

A data engineer needs to load data from an on-premises SQL Server database to Azure Synapse Analytics. The data volume is approximately 2 TB and the network bandwidth is limited. Which approach minimizes data transfer time?

Accepted Answer

Use Azure Data Box to physically ship the data.. Azure Data Box is the correct approach because it physically ships the 2 TB of data on a secure storage device, bypassing the limited network bandwidth entirely. For large data volumes (multiple TB) with constrained connectivity, offline data transfer is significantly faster than any online method, as it avoids network latency and bandwidth bottlenecks.

Answer

Use SQL Server Integration Services (SSIS) to transfer data over the internet.

Answer

Establish a site-to-site VPN and use Azure Data Factory.

Answer

Use Azure Data Factory with a self-hosted integration runtime over the internet.

Question 11

Your company wants to use Microsoft Fabric to create a unified analytics platform. Which component in Microsoft Fabric provides a lake-centric, collaborative, and governed data foundation?

Accepted Answer

OneLake. OneLake is the correct answer because it is the single, unified, lake-centric data foundation in Microsoft Fabric. It provides a multi-cloud, SaaS-based data lake that is automatically provisioned for every Fabric tenant, enabling collaborative and governed access to data without data duplication, while supporting open formats like Delta Parquet.

Answer

Power BI

Answer

Data Factory

Answer

Synapse Data Engineering

Question 12

A data analyst is using Azure Databricks to transform streaming data from Event Hubs. They need to ensure that if a failure occurs, the streaming job can resume processing from the last committed offset. Which checkpointing mechanism should they configure?

Accepted Answer

Use Structured Streaming with checkpointing to Azure Data Lake Storage Gen2.. Structured Streaming in Azure Databricks uses checkpointing to store the current state and offsets of the streaming query in a reliable, external storage system. By configuring checkpointing to Azure Data Lake Storage Gen2, the job can recover from failures and resume processing exactly from the last committed offset, ensuring exactly-once semantics. This is the recommended approach for production streaming workloads on Azure.

Answer

Enable write-ahead logs on the Event Hubs namespace.

Answer

Use checkpointing to Hive metastore.

Answer

Use checkpointing to DBFS (Databricks File System).

Question 13

Your organization uses Azure Purview to scan data sources. You need to set up a scan rule set that automatically classifies credit card numbers in Azure SQL Database. Which built-in classification rule should you enable?

Accepted Answer

Enable the 'Credit Card Number' classification.. Azure Purview includes a built-in 'Credit Card Number' classification rule that uses a predefined regular expression pattern to detect credit card numbers in data sources like Azure SQL Database. Enabling this rule automatically classifies the data without requiring custom development, aligning with the requirement to use a built-in classification.

Answer

Use a regular expression pattern matching.

Answer

Create a custom classification rule.

Answer

Enable the 'Personally Identifiable Information (PII)' classification.

Question 14

A company runs a critical workload in Azure Synapse Analytics. They need to ensure that if a single node fails, the data in the control node and compute nodes is not lost. Which configuration should they use?

Accepted Answer

No additional configuration is needed; Synapse provides built-in fault tolerance.. Azure Synapse Analytics (formerly SQL DW) is a distributed MPP (Massively Parallel Processing) system that automatically replicates data across multiple internal copies within the control node and compute nodes. This built-in fault tolerance ensures that if a single node fails, data is not lost because Synapse maintains at least three synchronous replicas of all data and metadata. No additional configuration is required for node-level failure protection.

Answer

Deploy a SQL Server Always On availability group.

Answer

Configure active geo-replication.

Answer

Use a Windows Server Failover Cluster.

Question 15

A data analyst needs to create a real-time dashboard in Power BI that refreshes every second from an Azure Stream Analytics job. Which Power BI feature should they use?

Accepted Answer

Streaming dataset. B is correct because a streaming dataset in Power BI is designed to ingest real-time data from sources like Azure Stream Analytics and automatically update visuals as new data arrives. This feature supports push-based updates at sub-second intervals, making it ideal for a dashboard that refreshes every second without requiring manual or scheduled refresh cycles.

Answer

Scheduled refresh

Answer

DirectQuery

Answer

Import mode