DP-900 Practice Test 15 — 15 Questions

Question 1

A media company needs to store thousands of high-resolution videos. Each video is up to 10 GB in size and must be accessible via HTTP/HTTPS URLs for playback. The company does not require a file system hierarchy or SMB protocol support. Which Azure storage solution is most appropriate for this scenario?

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

Accepted Answer

Azure Blob Storage. Azure Blob Storage is designed for storing massive amounts of unstructured data, such as high-resolution videos, and provides HTTP/HTTPS access via URLs. It supports objects up to 4.77 TiB (or larger with premium block blobs), easily accommodating 10 GB files, and offers no file system hierarchy or SMB protocol, matching the company's requirements exactly.

Answer

Azure Files

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Azure Queue Storage

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Azure Table Storage

Question 2

You need to query data stored in Azure Cosmos DB for NoSQL using SQL-like syntax. Which feature should you use?

Accepted Answer

Use the SQL API built into Cosmos DB. Azure Cosmos DB for NoSQL provides a native SQL API that allows you to query JSON documents using SQL-like syntax. This API translates standard SQL queries into Cosmos DB's internal query engine, enabling you to SELECT, filter, and project data directly from containers without any additional services or connectors.

Answer

Use Azure SQL Database elastic query

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Use Power BI DirectQuery

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Use Azure Synapse Analytics Serverless SQL pool

Question 3

Your company runs a global e-commerce platform that generates over 5 TB of clickstream data daily. The data is currently stored as raw CSV files in Azure Blob Storage. The data engineering team needs to transform this data into a star schema for business intelligence reporting. They want to use a serverless, code-first approach where they can write Python or SQL transformations. The transformed data should be stored in a format that optimizes query performance for Power BI. You also need to ensure that the solution can handle variable data volumes without manual scaling. Which Azure service should you use for the transformation?

Accepted Answer

Azure Databricks. Azure Databricks is the correct choice because it provides a serverless, code-first environment where data engineers can write Python or SQL transformations using Apache Spark. It can handle variable data volumes without manual scaling, and it can output transformed data in optimized formats like Parquet, which significantly improves query performance for Power BI. This aligns perfectly with the requirement for a serverless, code-first approach and star schema transformation.

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Azure Stream Analytics

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Azure Synapse Serverless SQL

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Azure Data Factory

Question 4

Which TWO of the following are benefits of using Azure Table Storage over Azure Blob Storage for storing semi-structured data?

Accepted Answer

Supports querying by partition key and row key. Table Storage supports key-value access and automatic indexing of partition and row keys, making queries by key efficient. Blob Storage is for unstructured data and does not provide built-in key-based querying. Both have REST APIs. Blob Storage has higher throughput for large files.

Answer

Provides automatic indexing of all attributes

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Supports REST API access

Answer

Offers higher throughput for large files

Question 5

Refer to the exhibit. You are reviewing an ARM template for an Azure SQL Database deployment. The database must support a read-only workload that requires low latency. The current configuration uses General Purpose tier with 4 vCores. What is the most significant performance improvement you can make without changing the tier?

Accepted Answer

Enable read scale-out by adding 'readScale' property. Enabling read scale-out by adding the 'readScale' property allows the database to use a read-only replica, offloading read workloads from the primary and providing low-latency reads. This is the most significant performance improvement within the General Purpose tier because it directly addresses the read-only workload requirement without changing the tier or incurring additional compute costs.

Answer

Increase maxSizeBytes to 1 TB

Answer

Set the edition to 'Serverless'

Answer

Change requestedBackupStorageRedundancy to 'Local'

Question 6

A company needs to analyze streaming data from IoT devices in real time. They want to identify anomalies and trigger alerts. Which Azure service should they use as the core processing engine?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is purpose-built for real-time stream processing, allowing you to define SQL-like queries that run continuously against streaming data from sources like IoT Hub. It can detect anomalies and trigger alerts on the fly, making it the correct core processing engine for this IoT scenario.

Answer

Azure Synapse Analytics

Answer

Azure Databricks

Answer

Azure Data Lake Storage

Question 7

You are a data architect for a healthcare organization. The organization needs to build a real-time analytics solution to monitor patient vital signs from IoT devices. The data arrives at a rate of 10,000 events per second. Each event contains patient ID, timestamp, heart rate, blood pressure, and oxygen saturation. The solution must alert clinicians within 10 seconds when a patient's vital signs exceed predefined thresholds. Additionally, the solution must store the raw data for historical analysis and compliance. You plan to use Azure Event Hubs for ingestion. Which combination of services should you use to meet the requirements? Consider: processing low latency alerts, storing raw data in cost-effective storage, and enabling historical analytics. You also need to ensure that the solution can scale to handle future growth.

Accepted Answer

Use Azure Stream Analytics for real-time processing and alerting, output data to Azure Data Lake Storage, and use Azure Synapse Serverless SQL for historical analytics. Azure Stream Analytics provides low-latency (sub-second) stream processing and can trigger alerts within the 10-second requirement. Outputting raw data to Azure Data Lake Storage (ADLS) offers cost-effective storage for compliance, and Azure Synapse Serverless SQL enables on-demand historical analytics without provisioning dedicated compute, scaling automatically for future growth.

Answer

Use Azure Databricks with Structured Streaming, store data in Delta Lake, and use Power BI for real-time dashboards

Answer

Use Azure Data Factory to batch ingest events every minute, store in Azure Blob Storage, and use Azure Analysis Services for historical analytics

Answer

Use Azure Functions to process events, store data in Azure Cosmos DB, and use Power BI for historical analytics

Question 8

Your company has a Power BI dashboard that uses a data model with a single large fact table and several dimension tables. The dashboard loads slowly when users filter by multiple dimensions. Which design change would MOST improve performance?

Accepted Answer

Ensure the fact table and dimension tables follow a star schema design with proper relationships.. A star schema design with proper relationships between the fact table and dimension tables is the foundational best practice for optimizing Power BI data models. This design minimizes the cardinality of relationships, reduces the size of the data model, and enables efficient query folding and storage engine compression, which directly improves filter performance across multiple dimensions.

Answer

Use page-level filters instead of report-level filters.

Answer

Create a calculated table that aggregates the fact table at a higher granularity.

Answer

Convert the data model to a composite model using DirectQuery for some tables.

Question 9

Your company stores customer data in Azure Blob Storage. To comply with data residency regulations, you must ensure data is replicated within the same Azure region. Which replication option should you choose?

Accepted Answer

Locally-redundant storage (LRS). Locally-redundant storage (LRS) replicates data three times within a single physical location in the same Azure region, ensuring data residency compliance by never copying data outside that region. This is the only option that guarantees all replicas stay within one region without any cross-region or cross-zone replication.

Answer

Zone-redundant storage (ZRS)

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Geo-redundant storage (GRS)

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Read-access geo-redundant storage (RA-GRS)

Question 10

You are implementing a data pipeline that ingests millions of events per second from IoT devices. The pipeline must tolerate failures and guarantee exactly-once processing. Which Azure service should you use to ingest the events?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is the correct choice because it is a big data streaming platform and event ingestion service designed for high-throughput scenarios, capable of ingesting millions of events per second. It supports exactly-once processing through checkpointing and partition-based offset management, and its built-in replication and availability zones provide fault tolerance.

Answer

Azure IoT Hub

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Azure Service Bus

Answer

Azure Queue Storage

Question 11

Refer to the exhibit. You are analyzing a message from an IoT device captured in Azure Event Hubs. The message contains system properties indicating the device ID and authentication method. You need to route messages from device-01 to a separate storage container for compliance. Which property should you use in a Stream Analytics query to filter messages?

Accepted Answer

iothub-connection-device-id. The `iothub-connection-device-id` system property is automatically added by Azure IoT Hub to every device-to-cloud message. In a Stream Analytics query, you can reference this property directly (e.g., `WHERE iothub-connection-device-id = 'device-01'`) to filter messages from a specific device for routing to a separate storage container for compliance.

Answer

partitionId

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consumerGroup

Answer

deviceId

Question 12

A company wants to store historical sales data for long-term analysis. The data is accessed infrequently but must be retained for 7 years. Which Azure storage tier minimizes cost while meeting these requirements?

Accepted Answer

Cool storage tier. The Cool storage tier is designed for data that is accessed infrequently but must be retained for extended periods, offering lower storage costs than Hot tier while still providing low-latency access when needed. With a 30-day minimum storage duration and a cost structure optimized for infrequent reads, it balances cost and accessibility for 7-year retention of historical sales data.

Answer

Archive storage tier

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Premium storage tier

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Hot storage tier

Question 13

A company ingests streaming data from thousands of devices into Azure Event Hubs. They need to transform and aggregate the data in real time before storing it in Azure Data Lake Storage Gen2. Which Azure service should they use between Event Hubs and ADLS Gen2?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is purpose-built for real-time data processing and analytics, allowing you to define SQL-like queries to transform and aggregate streaming data from Event Hubs before outputting it directly to Azure Data Lake Storage Gen2. It provides exactly-once delivery semantics and low-latency processing, making it the ideal service for this ingestion-to-storage pipeline.

Answer

Azure Functions

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Azure Databricks

Answer

Azure Data Factory

Question 14

A financial database system ensures that once a transaction is committed, the data changes are permanently stored and will survive any subsequent system failure, such as a power outage or crash. Which property of ACID transactions does this describe?

Accepted Answer

D: Durability. D is correct because durability guarantees that once a transaction is committed, the changes persist permanently, even in the event of a system failure like a power outage or crash. In SQL Server, this is implemented via the write-ahead log (WAL) and checkpoint processes, ensuring committed data is flushed to disk before acknowledging success.

Answer

A: Atomicity

Answer

B: Consistency

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C: Isolation

Question 15

A retail company collects streaming clickstream data from its website into Azure Event Hubs. They need to aggregate the data in real-time to count page views per product every minute and store the results in Azure SQL Database for a live dashboard. Which Azure service should they use to perform this real-time aggregation?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is purpose-built for real-time stream processing, allowing you to define a query that aggregates clickstream data from Event Hubs using a tumbling window of one minute to count page views per product. The result can be written directly to Azure SQL Database, enabling a live dashboard without additional orchestration.

Answer

Azure Data Factory

Answer

Azure Synapse Pipelines

Answer

Azure Databricks