DP-900 Practice Test 12 — 15 Questions

Question 1

A company needs to store JSON documents that are frequently updated by multiple services. The solution must support indexing and querying by any property. Which Azure data service should they use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a fully managed NoSQL database designed for JSON documents, offering native support for indexing every property automatically without requiring a predefined schema. Its multi-model API (including SQL API) allows querying by any property with low-latency reads and writes, making it ideal for services that frequently update JSON documents.

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Azure Blob Storage

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Azure SQL Database

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Azure Table Storage

Question 2

A logistics company stores shipment tracking data. The shipment ID, destination, and weight are stored in a fixed-schema database table. The shipment's route history is stored as a JSON document where each document can have different fields depending on the route events recorded. Which classification of data best describes the route history data?

Accepted Answer

Semi-structured data. The route history data is stored as JSON documents where each document can have different fields depending on the events recorded. This flexibility in schema—where fields vary per document—is the hallmark of semi-structured data. Unlike structured data with a fixed schema, semi-structured data uses tags or markers (like JSON key-value pairs) to organize the data, making it self-describing.

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Structured data

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Unstructured data

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Analytical data

Question 3

A retail company operates an online store. The store processes each customer's order immediately upon submission, updating inventory and payment records in real-time. Additionally, the company's business analysts run weekly reports that aggregate sales data over the past month to identify trends. Which of the following correctly describes the two workload types represented in this scenario?

Accepted Answer

The order processing is an OLTP workload; the weekly reporting is an OLAP workload.. The order processing system handles individual transactions (inserts/updates) in real-time, which is the hallmark of an Online Transaction Processing (OLTP) workload. The weekly reporting aggregates large volumes of historical data for trend analysis, which is an Online Analytical Processing (OLAP) workload. OLTP is optimized for high-volume, low-latency writes, while OLAP is optimized for complex read-heavy queries over large datasets.

Answer

The order processing is an OLAP workload; the weekly reporting is an OLTP workload.

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Both workloads are OLTP workloads.

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Both workloads are batch processing workloads.

Question 4

A retail company ingests clickstream data from its e-commerce website into Azure Event Hubs. They need to detect customer journey patterns in real time within seconds and also prepare aggregated data for daily trend reports stored in Azure Data Lake Storage Gen2. The real-time processing must handle high throughput and support complex temporal queries like sessionization. The daily aggregation should be cost-effective and use serverless compute. Which combination of Azure services should they use?

Accepted Answer

Azure Stream Analytics for real-time processing and Azure Data Factory for daily batch aggregation. Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.

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Azure Functions for real-time processing and Azure Databricks for daily batch aggregation

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Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation

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Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation

Question 5

A business analyst needs to explore and create interactive visualizations of sales data stored in Azure Data Lake Storage Gen2 without writing SQL code. Which Azure service is best suited for this drag-and-drop data exploration?

Accepted Answer

Microsoft Power BI. Microsoft Power BI is the correct choice because it provides a drag-and-drop interface for creating interactive visualizations and exploring data without requiring SQL code. It can directly connect to Azure Data Lake Storage Gen2 using Power Query or the ADLS connector, enabling business analysts to build reports and dashboards through intuitive visual interactions.

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Azure Stream Analytics

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Azure Data Factory

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Azure Databricks

Question 6

A company collects data from three sources: Source A: Customer records from a relational database with fixed columns (CustomerID, Name, Address). Source B: Social media posts in JSON format with varying fields (e.g., some posts have 'likes', others have 'shares'). Source C: Handwritten notes saved as scanned images in TIFF format. Which statement correctly categorizes the data by structure?

Accepted Answer

Source A: Structured, Source B: Semi-structured, Source C: Unstructured. Source A's relational database with fixed columns (CustomerID, Name, Address) enforces a strict schema, making it structured data. Source B's JSON format allows varying fields like 'likes' or 'shares' per record, which is the hallmark of semi-structured data (self-describing, schema-on-read). Source C's scanned TIFF images are binary blobs with no inherent internal structure for querying, classifying them as unstructured data. This matches the standard DP-900 categorization: structured (fixed schema), semi-structured (flexible schema), unstructured (no schema).

Answer

Source A: Structured, Source B: Unstructured, Source C: Semi-structured

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Source A: Semi-structured, Source B: Structured, Source C: Unstructured

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Source A: Semi-structured, Source B: Unstructured, Source C: Structured

Question 7

A data engineer is classifying data types collected from three sources for a data lake. Source 1: Customer records from a SQL database exported as CSV files with fixed columns (CustomerID, Name, Address). Source 2: Product reviews obtained via API as JSON documents with varying fields (e.g., some reviews include 'rating' and 'verified_purchase', others include 'comment'). Source 3: Scanned handwritten order forms saved as TIFF images. Which statement correctly categorizes these data by structure?

Accepted Answer

Source 1: Structured; Source 2: Semi-structured; Source 3: Unstructured. Source 1 (CSV from SQL) has a fixed schema with defined columns, making it structured data. Source 2 (JSON from API) allows varying fields per document, which is the hallmark of semi-structured data. Source 3 (TIFF images) contains no inherent schema or machine-readable structure, classifying it as unstructured data.

Answer

Source 1: Structured; Source 2: Structured; Source 3: Unstructured

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Source 1: Semi-structured; Source 2: Structured; Source 3: Unstructured

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Source 1: Structured; Source 2: Unstructured; Source 3: Semi-structured

Question 8

A company stores backup files in Azure Blob Storage. The backup files are accessed frequently for the first 30 days, then only rarely for the next six months. After one year, the files must be retained for compliance but are never accessed. The company wants to minimize storage costs. Which solution should they use?

Accepted Answer

Use Azure Blob Storage lifecycle management policies. Azure Blob Storage lifecycle management policies allow you to automatically transition blobs to cooler tiers (e.g., from Hot to Cool after 30 days, then to Archive after one year) and delete blobs after a specified period, all without manual intervention. This directly matches the access pattern: frequent access for 30 days, rare access for six months, and never accessed after one year, minimizing storage costs by using the most cost-effective tier for each phase.

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Manually move files between storage accounts

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Use Azure File Sync

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Use Azure NetApp Files

Question 9

A company runs an e-commerce application on Azure SQL Database. The application experiences unpredictable traffic spikes during flash sales and promotional events. The company wants to automatically scale compute resources based on actual demand and pay only for the resources consumed. Which Azure SQL Database deployment option best meets these requirements?

Accepted Answer

Serverless. The Serverless deployment option for Azure SQL Database automatically scales compute resources (vCores) based on actual demand, pausing the database during idle periods and resuming on the first connection. This model charges per second for the compute used, making it ideal for unpredictable traffic spikes like flash sales, as it eliminates the need to over-provision and ensures you pay only for consumed resources.

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Provisioned DTU

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Provisioned vCore

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Hyperscale

Question 10

A startup is building a mobile app that allows users to share short text updates. Each update includes a user ID, timestamp, and message text. The development team expects rapid growth and needs a storage solution that can scale horizontally, handle high write throughput, and provide low-latency reads globally. Which Azure data service is most appropriate?

Accepted Answer

Azure Cosmos DB with a multi-master configuration and partition on user ID.. Azure Cosmos DB with a multi-master configuration is the most appropriate choice because it provides global distribution with multiple write regions, enabling horizontal scaling and low-latency reads and writes worldwide. Partitioning on user ID ensures even data distribution and efficient query performance for the app's high write throughput requirements.

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Azure SQL Database with a single database.

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Azure Blob Storage with append blobs.

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Azure Table Storage with user ID as partition key and timestamp as row key.

Question 11

A financial analytics company has petabytes of transaction data stored as Parquet files in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple tables and return results within seconds. The company wants to query the data directly without moving it to another store. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is correct because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving or loading the data. It uses a distributed query engine that can process petabytes of Parquet files and return results in seconds by leveraging pushdown computation and columnar storage formats.

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Azure SQL Database

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Azure HDInsight

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Azure Databricks

Question 12

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.

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Azure Logic Apps

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Azure Functions

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Azure Data Factory

Question 13

A data scientist needs to analyze historical sales data to identify yearly trends. They run SQL queries that aggregate millions of rows. No new data is being added during analysis. Which type of data processing workload does this represent?

Accepted Answer

Online Analytical Processing (OLAP). This workload is Online Analytical Processing (OLAP) because the data scientist is running complex SQL queries that aggregate millions of rows of historical sales data to identify yearly trends. OLAP is designed for read-intensive, analytical queries that summarize large volumes of static data, which matches the scenario where no new data is being added during analysis.

Answer

Online Transaction Processing (OLTP)

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Batch processing

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Stream processing

Question 14

A company stores IoT sensor data as JSON files in Azure Blob Storage. A data analyst needs to run ad-hoc SQL queries on these files without moving the data and without provisioning any compute clusters. The analyst wants to pay only for the amount of data processed by each query. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool allows you to query data directly from Azure Blob Storage using T-SQL without provisioning any compute clusters. It uses a pay-per-query model where you are billed only for the amount of data processed, making it ideal for ad-hoc SQL queries on JSON files stored in Blob Storage without data movement.

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Azure SQL Database

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Azure Cosmos DB

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Azure Data Factory

Question 15

A financial services company stores account balances in Azure SQL Database (strong consistency) and transaction audit logs in Azure Cosmos DB (eventual consistency by default). A compliance requirement demands that when a transaction is rolled back in the SQL database, the corresponding audit log entries in Cosmos DB must also be removed within a short time frame. Which term best describes the difficulty of maintaining this constraint?

Accepted Answer

Distributed transaction coordination. The scenario requires coordinating a rollback across two distinct data stores—Azure SQL Database (ACID-compliant, strong consistency) and Azure Cosmos DB (eventual consistency by default). This cross-system transactional consistency is a classic distributed transaction coordination problem, often addressed via patterns like the two-phase commit (2PC) or the saga pattern, but not natively supported between these two services without custom orchestration.

Answer

ACID compliance

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Idempotency

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Schema flexibility