DP-900 Practice Test 49 — 15 Questions

Question 1

A media company stores large video files and associated metadata (title, duration, tags) as JSON documents. The application requires low-latency streaming of videos to users worldwide and the ability to quickly query metadata by tag. Which combination of Azure services should the company use?

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

Accepted Answer

Azure Blob Storage for videos and Azure Cosmos DB for metadata. Azure Blob Storage is optimized for storing large binary objects like video files, offering high-throughput streaming via HTTP/HTTPS and integration with CDN for low-latency global delivery. Azure Cosmos DB provides single-digit millisecond read and write latencies with automatic indexing, making it ideal for quickly querying JSON metadata by tag using SQL or MongoDB API. This combination separates storage concerns (blobs for raw video, document DB for structured metadata) to meet both streaming and query performance requirements.

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Azure Blob Storage for both videos and metadata

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Azure Cosmos DB for videos and Azure Table Storage for metadata

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Azure Files for videos and Azure SQL Database for metadata

Question 2

You need to store telemetry data from millions of devices. Each record includes a device ID, timestamp, and metric value. The data will be queried by device ID and time range. Which Azure data store is best suited for this scenario?

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is a fully managed, high-performance analytics service optimized for ingesting and querying large volumes of telemetry and log data using Kusto Query Language (KQL). It is purpose-built for time-series and IoT scenarios where data is queried by device ID and time range, offering sub-second query performance over billions of records.

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Azure SQL Database

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Azure Storage Queues

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Azure Cosmos DB

Question 3

You are a data engineer for a financial services company. The company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. They have a fact table named Transactions that contains 2 billion rows. The table is hash-distributed on the AccountID column. Users run reports that aggregate transaction amounts by date and account type. The reports are slow. Upon investigation, you find that the distribution is highly skewed because a few accounts have millions of transactions. You need to improve query performance without redesigning the entire schema. Which action should you take?

Accepted Answer

Change the distribution key to a column with more unique values, such as TransactionID. Changing the distribution key to TransactionID, which has far more unique values than AccountID, will eliminate the data skew that is causing performance degradation. In a hash-distributed table, a skewed distribution key leads to some distributions holding a disproportionate amount of data, causing parallel query execution to be bottlenecked by the largest distribution. By using a highly unique column like TransactionID, the data will be evenly distributed across all 60 distributions, enabling balanced parallelism and faster aggregation queries.

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Change the distribution to round-robin

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Create a clustered columnstore index on the table

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Replicate the Transactions table to all distributions

Question 4

A gaming company stores player profiles as JSON documents. Each profile can have different attributes; for example, some profiles include an 'achievements' field while others include a 'purchaseHistory' field. The application must retrieve profiles by player ID with single-digit-millisecond latency and also support SQL-like queries on any attribute. Which Azure data store should the company use?

Accepted Answer

B. Azure Cosmos DB Core (SQL) API. Azure Cosmos DB Core (SQL) API is the correct choice because it natively stores JSON documents with flexible schemas, supports indexing on any attribute for SQL-like queries, and guarantees single-digit-millisecond latency for point reads by player ID. This meets the requirement for both fast key-based lookups and ad-hoc querying across varying profile attributes.

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A. Azure Table Storage

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C. Azure Blob Storage

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D. Azure Database for PostgreSQL

Question 5

A retail company wants to run real-time analytics on streaming clickstream data from their website. Which Azure service should they use to ingest and process the data?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is a real-time analytics and event-processing engine designed to ingest, process, and analyze high-velocity streaming data, such as clickstream data from a website. It can directly consume data from Azure Event Hubs or IoT Hub and output results to sinks like Power BI, Azure SQL Database, or Azure Data Lake Storage, making it the correct choice for real-time analytics on streaming data.

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Azure Analysis Services

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Azure Data Lake Storage

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Azure SQL Database

Question 6

A retail company uses Azure SQL Database to store transactional data. They need to ensure that reporting queries do not impact the performance of the transactional workload. Which solution should you recommend?

Accepted Answer

Configure a read replica in Azure SQL Database. A read replica in Azure SQL Database allows reporting queries to be offloaded to a read-only copy of the database, isolating them from the primary transactional workload. This ensures that reporting activities do not consume resources (CPU, IO, memory) on the primary instance, preventing performance degradation for transactional operations.

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Increase the DTU or vCore limit of the database

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Add indexes to the reporting tables

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Partition the largest tables by date

Question 7

You are designing a data solution for a healthcare application that requires ACID transactions for patient records and needs to run complex analytics queries. Which combination of Azure services should you recommend?

Accepted Answer

Azure SQL Database for transactions, Azure Synapse Analytics for analytics. Azure SQL Database provides full ACID (Atomicity, Consistency, Isolation, Durability) transaction support, which is essential for healthcare patient records where data integrity is critical. Azure Synapse Analytics is a cloud-based analytics service that can run complex queries against large datasets, including those from Azure SQL Database, using its massively parallel processing (MPP) architecture. This combination allows transactional and analytical workloads to coexist without compromising performance or consistency.

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Azure Cosmos DB for transactions, Power BI for analytics

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Azure Database for MySQL for transactions, Azure Analysis Services for analytics

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Azure Blob Storage for transactions, Azure Machine Learning for analytics

Question 8

A mobile gaming company stores player activity logs as JSON documents. Each document has a unique ActivityID, a PlayerID, a timestamp, and a variable set of attributes depending on the game event (e.g., level started, item purchased). The application requires low-latency point reads by ActivityID and needs to query logs by PlayerID for a given time range. Schema flexibility is critical because new game events are added frequently. Which Azure Cosmos DB API should they choose?

Accepted Answer

NoSQL API (formerly SQL API). The NoSQL API (formerly SQL API) is the correct choice because it natively supports JSON documents with flexible schemas, enabling the variable attributes required for new game events. It provides low-latency point reads by ActivityID via direct partition key lookups and supports efficient queries by PlayerID within a time range using composite indexes or cross-partition queries with filtering. This API is optimized for schema-agnostic, document-based workloads and offers the richest query capabilities for JSON data in Azure Cosmos DB.

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MongoDB API

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Cassandra API

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Gremlin API

Question 9

A retail company wants to store product catalog data in a non-relational format. The data includes product ID, name, description, price, and an array of tags. The data is frequently updated and must support low-latency reads and writes at global scale. Which Azure service should they use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a globally distributed NoSQL database service that supports low-latency reads and writes at global scale. It can store JSON documents with arrays, making it suitable for product catalog data that includes tags. Azure Table Storage is a key-value store but lacks global distribution and support for complex queries. Azure Blob Storage is designed for unstructured blob data, not transactional updates. Azure Cache for Redis is an in-memory cache, not a durable data store for product catalogs.

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Azure Table Storage

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Azure Blob Storage

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Azure Cache for Redis

Question 10

A social media company stores user posts as JSON documents in Azure Cosmos DB. Each post may have a different number of fields and nested objects. Which type of data model does this represent?

Accepted Answer

Document. The scenario describes user posts stored as JSON documents with varying fields and nested objects. Azure Cosmos DB's Document data model (using the SQL API or MongoDB API) is designed for semi-structured, schema-agnostic data where each document can have a different structure, making it the correct choice.

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Key-value

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Column-family

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Graph

Question 11

A media company stores video metadata in Azure Table Storage. Each video has a unique VideoID, and the application frequently queries for videos uploaded on a specific date. The current table uses PartitionKey = VideoID and RowKey = UploadDate. Queries filtering by UploadDate are slow and consume many transactions. Which design change will most optimize queries that retrieve all videos from a given date?

Accepted Answer

C. Change the PartitionKey to a date-based value (e.g., YYYY-MM-DD) and use VideoID as the RowKey.. Azure Table Storage queries are most efficient when the PartitionKey is used as the primary filter. By changing the PartitionKey to a date-based value (e.g., YYYY-MM-DD), queries for all videos uploaded on a specific date become partition scans, which are fast and consume minimal transactions. Using VideoID as the RowKey still allows unique identification of each video within that date partition.

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A. Use UploadDate as the RowKey only, but keep PartitionKey as VideoID.

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B. Create a secondary index on UploadDate.

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D. Migrate the data to Azure Cosmos DB Table API for better indexing.

Question 12

You are a business analyst at a manufacturing company. The company uses Azure SQL Database to store production data. You need to create a Power BI report that shows real-time machine efficiency. The report must refresh every 5 minutes to show current metrics. You have been granted read-only access to the database. The database is under heavy load from transactional applications, and you want to minimize additional impact. Which approach should you take to create the report?

Accepted Answer

Use Power BI Import mode with incremental refresh policy to load only new data every 5 minutes. B is correct because Power BI Import mode with an incremental refresh policy allows you to load only new or changed data every 5 minutes, minimizing the load on the heavily used Azure SQL Database. This approach avoids repeated full table scans, reduces query impact, and still provides near-real-time metrics for the report.

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Disable the Power BI Query Cache to reduce database load

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Configure Power BI to use DirectQuery mode to ensure real-time data

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Export the data to a CSV file and import it into Power BI daily

Question 13

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.

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Create a view that filters on date and sensor ID

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Read all Parquet files and filter using a WHERE clause

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Create an index on the date and sensor ID columns

Question 14

Which TWO Azure services can be used to build a data pipeline that moves data from on-premises SQL Server to Azure Synapse Analytics?

Accepted Answer

Azure Data Factory. Azure Data Factory (A) is correct because it is a cloud-based ETL and data integration service that provides built-in connectors for both on-premises SQL Server (via self-hosted integration runtime) and Azure Synapse Analytics, enabling you to create, schedule, and orchestrate data pipelines that move and transform data between these sources.

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Azure Machine Learning

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Azure Stream Analytics

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Azure Analysis Services

Question 15

A company collects customer feedback in three forms: a structured table with customer ID and rating (1-5), free-text comments, and audio recordings of phone calls. Which of the following correctly orders these data from least structured to most structured?

Accepted Answer

Audio recordings, free-text comments, structured table. Ly orders the data from least structured to most structured. Audio recordings are unstructured (no schema). Free-text comments are also unstructured (no fixed format in DP-900 classification). The structured table has defined columns and data types. Thus, the order is audio (unstructured), free-text (unstructured), structured table.

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Structured table, free-text comments, audio recordings

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Free-text comments, structured table, audio recordings

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Audio recordings, structured table, free-text comments