DP-900 Practice Test 5 — 10 Questions

Question 1

A company ingests raw clickstream data as JSON files into Azure Data Lake Storage Gen2. Data scientists need to explore the data interactively using Python notebooks, and the BI team needs to create reports from aggregated datasets derived from this data. The solution must be serverless, scale automatically, and minimize administration. Which Azure service should they choose?

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

Accepted Answer

B. Azure Databricks. Azure Databricks is correct because it provides a serverless, interactive Apache Spark environment that data scientists can use with Python notebooks for exploratory analysis, and it can produce aggregated datasets for BI reporting. It scales automatically and minimizes administration by managing the cluster lifecycle, making it ideal for ad-hoc data exploration on raw JSON files in Azure Data Lake Storage Gen2.

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A. Azure Synapse Analytics (serverless SQL pool)

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C. Azure HDInsight with Spark

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D. Azure Data Lake Analytics

Question 2

A retail company operates an e-commerce website that processes customer orders (insert, update, delete) throughout the day. The same company also runs reports on sales trends at the end of each quarter. Which type of data processing workload does the order processing represent?

Accepted Answer

B) Transactional processing (OLTP). Order processing involves inserting, updating, and deleting individual customer orders in real time as they occur. This is the classic definition of an Online Transaction Processing (OLTP) workload, which is optimized for high-volume, low-latency transactions that maintain ACID (Atomicity, Consistency, Isolation, Durability) properties. The e-commerce website requires immediate data consistency for each order, which is the hallmark of transactional processing.

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A) Batch processing

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C) Analytical processing (OLAP)

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D) Stream processing

Question 3

A company updates a customer's address in a database. The update must ensure that all existing orders still reference a valid customer ID. The database checks the foreign key constraint and rejects the update if it would violate referential integrity. Which ACID property does this enforcement represent?

Accepted Answer

Consistency. Consistency ensures that any database transaction brings the database from one valid state to another, preserving all defined rules, including constraints like foreign keys. In this scenario, the foreign key constraint enforcement prevents an update that would leave orphaned order records, directly upholding the consistency property by rejecting the transaction if it violates referential integrity.

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Atomicity

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Isolation

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Durability

Question 4

A large e-commerce company needs to build an analytics solution. They have streaming clickstream data from their website (JSON) and daily sales data from their transactional database (CSV). They need to perform real-time dashboards on clickstream for the current hour, and also run complex historical queries that join sales data with aggregated clickstream data over the past year. They want a single Azure service that can handle both stream processing and batch processing using a unified experience, without moving data between separate systems. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified experience for both stream processing (via Synapse Pipelines or Spark Structured Streaming) and batch processing (via dedicated SQL pools or serverless SQL), enabling real-time dashboards on clickstream data and complex historical queries joining sales data with aggregated clickstream data without moving data between separate systems.

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Azure Stream Analytics

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Azure Data Factory

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Azure HDInsight

Question 5

A manufacturing company collects real-time temperature data from thousands of IoT sensors. They need to build an analytics solution that processes the streaming data, computes the average temperature per device every minute, and outputs the results to a Power BI dashboard for near real-time visualization. Which Azure service should they use for the real-time stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a fully managed, real-time stream processing engine designed specifically for scenarios like this: ingesting high-velocity data from IoT sensors, performing time-windowed aggregations (e.g., average temperature per device every minute), and outputting results directly to Power BI for near real-time dashboards. It natively supports SQL-like query language for defining windowed computations and has built-in connectors for both IoT Hub/Event Hubs (input) and Power BI (output), making it the most efficient and purpose-built service for this streaming analytics workload.

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Azure Data Factory

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Azure Databricks

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Azure SQL Database

Question 6

A retail company receives real-time data from IoT sensors in its warehouses. Each sensor sends a JSON payload containing a device ID, timestamp, and temperature reading. A data engineer needs to classify this data for storage planning. Which data type best describes the JSON payload?

Accepted Answer

Semi-structured data. The JSON payload is considered semi-structured data because it has organizational properties (key-value pairs, nested structure) that provide a schema, but it does not conform to a rigid tabular schema like a relational database. JSON allows flexible fields and varying data types, which is characteristic of semi-structured data.

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Structured data

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Unstructured data

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Relational data

Question 7

An e-commerce company has 20 SQL Server databases that each range from 10 GB to 50 GB and experience unpredictable usage patterns with occasional spikes in user activity. The company wants to migrate to Azure SQL Database to reduce management overhead and minimize costs by allowing databases to share resources. Which Azure SQL Database deployment option should they choose?

Accepted Answer

B: Elastic pool. Elastic pools allow multiple SQL databases to share a fixed set of resources (eDTUs or vCores), which is ideal for databases with unpredictable usage patterns and occasional spikes. By pooling resources, the company can reduce management overhead and minimize costs compared to provisioning each database individually, as the pool's total resources are shared among all databases, smoothing out peak demands.

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A: Single database with provisioned throughput

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C: Managed Instance

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D: SQL Server on Azure VM

Question 8

A media company stores large video files in Azure Blob Storage. The videos are accessed frequently for the first 30 days after upload, then rarely for the next 180 days. After that, they are only needed for compliance and are never accessed. Which access tier should be used for the first 30 days to minimize costs while maintaining low latency?

Accepted Answer

Hot tier. The Hot tier is the correct choice for the first 30 days because it provides the lowest latency access and highest throughput for frequently accessed data, which matches the requirement of frequent access during this period. While the Hot tier has the highest storage cost per GB, it has no retrieval costs, making it cost-effective for high-access patterns. The other tiers introduce either retrieval fees (Cool), high latency (Archive), or unnecessary cost (Premium) for this use case.

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Cool tier

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Archive tier

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Premium tier

Question 9

A hospital collects patient vital signs every minute using IoT sensors. Each reading contains a timestamp, patient ID, heart rate, blood pressure, and temperature. This data is ingested continuously for real-time monitoring and alerting. Which type of data workload does this scenario best represent?

Accepted Answer

D. Real-time streaming. This scenario requires continuous ingestion of sensor data with immediate processing for real-time monitoring and alerting. Real-time streaming workloads, such as those handled by Azure Stream Analytics or Apache Kafka, are designed to process unbounded data streams with low latency, making option D correct.

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A. Transactional workload

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B. Analytical workload

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C. Batch processing

Question 10

A company wants to build a modern data warehouse using a lakehouse architecture. They need to store raw data in its native format (e.g., CSV, JSON, Parquet) and also support BI reporting on curated, transformed data. They want to use a single storage layer for both raw and curated data. Which Azure service should they use as the core storage layer?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it provides a single, unified storage layer that can store raw data in its native format (CSV, JSON, Parquet) in a hierarchical namespace, while also serving as the foundation for curated, transformed data used in BI reporting. It combines the scalability and cost-effectiveness of Azure Blob Storage with the file system semantics and ACLs needed for analytics workloads, making it the ideal core storage layer for a lakehouse architecture.

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Azure SQL Database

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Azure Synapse Analytics

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Azure Cosmos DB