DP-900 Practice Test 10 — 10 Questions

Question 1

A company uses Azure SQL Database for a customer management system. The Customers table has columns: CustomerID (int, primary key), FullName (varchar(100)), Email (varchar(200)), SignUpDate (date), LastLoginDate (date). Queries frequently filter on LastLoginDate to find customers who have not logged in for over a year for a promotional campaign. The table has 10 million rows. Which type of index should they create to optimize these queries?

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

Accepted Answer

Non-clustered index on LastLoginDate. A non-clustered index on LastLoginDate allows the query to quickly locate rows where LastLoginDate is older than one year without scanning the entire 10-million-row table. Azure SQL Database uses B-tree structures for non-clustered indexes, enabling efficient range scans and key lookups for the filtered rows. This directly supports the promotional campaign query pattern.

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Clustered index on CustomerID

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Non-clustered index on FullName

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Columnstore index on SignUpDate and LastLoginDate

Question 2

A marketing team wants to analyze social media sentiment in near real-time. They will use Azure Event Hubs to capture tweets and need to aggregate sentiment scores over 5-minute windows. The aggregated results must be stored in Azure Blob Storage for later analysis. Which Azure service should they use to perform the stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is a real-time stream processing engine designed to ingest data from sources like Azure Event Hubs, apply temporal aggregations (e.g., 5-minute tumbling windows), and output results directly to Azure Blob Storage. It provides built-in support for windowed functions and exactly-once delivery semantics, making it ideal for near-real-time sentiment analysis without requiring custom code.

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Azure Data Factory

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Azure Databricks

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Azure Synapse Analytics

Question 3

A company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every day, they need to incrementally load 100 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2 (ADLS Gen2). The load should use PolyBase for efficient parallel data transfer and must be orchestrated on a recurring schedule. Which Azure service should they use to create and manage this pipeline?

Accepted Answer

A: Azure Data Factory. Azure Data Factory (ADF) is the correct choice because it provides native orchestration and scheduling capabilities for data pipelines. It supports PolyBase as a sink to load data into Azure Synapse dedicated SQL pool in parallel, and it can directly read CSV files from ADLS Gen2. ADF's built-in triggers allow you to schedule the daily incremental load without additional coding.

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B: Azure Stream Analytics

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C: Azure Databricks

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D: Azure Logic Apps

Question 4

A marketing company collects real-time clickstream data from their website using Azure Event Hubs. They need to perform two tasks: (1) aggregate the number of clicks per advertising campaign every 5 minutes and display the results in a live dashboard, and (2) run complex historical queries on months of aggregated click data to identify trends. They want to minimize data movement and use serverless compute where possible. Which combination of Azure services should they use?

Accepted Answer

Azure Stream Analytics for live aggregation and Power BI for the dashboard; Azure Synapse Analytics (serverless SQL pool) for historical queries. Azure Stream Analytics is ideal for real-time aggregation of clickstream data from Event Hubs, outputting to Power BI for a live dashboard. Azure Synapse Analytics serverless SQL pool allows querying months of aggregated data stored in Azure Data Lake Storage without provisioning compute, minimizing data movement and using serverless compute.

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Azure Data Factory for live aggregation; Azure Analysis Services for historical queries

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Azure HDInsight (Spark) for both live and historical processing

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Azure Functions for real-time aggregation; Azure SQL Database for historical queries

Question 5

A logistics company collects data from fleet sensors. Each sensor sends a JSON message containing the vehicle ID, timestamp, and a variable set of measurements such as engine temperature, tire pressure, and fuel level. The structure of the JSON message differs between sensor types and sometimes includes optional fields. How should this data be classified?

Accepted Answer

Semi-structured data. The data is semi-structured because it conforms to a schema (JSON format with fields like vehicle ID and timestamp) but allows variability in structure, such as optional fields and different sets of measurements per sensor type. This flexibility is a hallmark of semi-structured data, which does not require a rigid tabular schema like structured data but still contains tags or markers to separate data elements.

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Structured data

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Unstructured data

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Relational data

Question 6

A company ingests streaming data from IoT devices into Azure Event Hubs. They need to perform real-time analytics on the data, such as aggregating temperature readings over 5-minute windows and triggering alerts when thresholds are exceeded. They also want to store the processed data in a data warehouse for historical analysis. Which Azure service should they use for the real-time processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is purpose-built for real-time stream processing, allowing you to define SQL-like queries that aggregate data over tumbling or hopping windows (e.g., 5-minute windows) and trigger alerts based on thresholds. It integrates directly with Azure Event Hubs as a source and can output processed results to Azure Synapse Analytics or other data warehouses for historical storage, making it the correct choice for this real-time analytics workload.

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Azure Data Factory

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Azure Databricks

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Azure Logic Apps

Question 7

A financial company is migrating a 2-TB on-premises SQL Server database to Azure. The database uses SQL Server Agent jobs for data validation and cleanup, and it performs cross-database queries using three-part names (e.g., DB1.schema.table). The company requires a fully managed PaaS service that supports these features with minimal application changes. Which Azure SQL service should they choose?

Accepted Answer

Azure SQL Managed Instance. Azure SQL Managed Instance is the correct choice because it provides near-100% compatibility with on-premises SQL Server, including support for SQL Server Agent jobs and cross-database queries using three-part names (e.g., DB1.schema.table). As a fully managed PaaS service, it minimizes application changes while offloading infrastructure management, making it ideal for migrating a 2-TB database with these specific requirements.

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Azure SQL Database (single database)

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Azure SQL Database elastic pool

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SQL Server on Azure Virtual Machine

Question 8

A retail company uses a point-of-sale (POS) system that records each sales transaction in a database. Each transaction involves reading the current inventory, updating the stock level, and recording the sale. The database must ensure that concurrent transactions do not interfere with each other, so that one transaction does not see partially updated data from another. Which property of a database transaction ensures this isolation?

Accepted Answer

Isolation. Isolation ensures that concurrent transactions do not interfere with each other, so each transaction sees a consistent snapshot of the database as if it were the only transaction running. In the POS scenario, isolation prevents one transaction from reading partially updated inventory data from another transaction, which could lead to overselling or stock discrepancies. This property is typically implemented through locking mechanisms or multi-version concurrency control (MVCC).

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Atomicity

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Consistency

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Durability

Question 9

A company stores customer reviews for an e-commerce site. Each review contains a product ID, user ID, rating, and optional comments and images. The reviews are written once and rarely updated. The company needs to query reviews by product ID with low latency and also perform simple key-value lookups. They want a cost-effective, serverless solution that requires no scaling management. Which Azure data store should they choose?

Accepted Answer

Azure Table Storage. Azure Table Storage is a cost-effective, serverless NoSQL key-value store that supports simple key-value lookups and querying by partition key (e.g., ProductID) with low latency. It requires no scaling management, as it automatically scales based on demand, and is ideal for immutable, rarely-updated data like customer reviews. The pay-per-request pricing model makes it highly cost-effective for this workload.

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Azure Cosmos DB SQL API

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Azure Blob Storage

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Azure SQL Database

Question 10

A retail company stores product inventory data in a SQL database, customer reviews as JSON files, and product images as JPEG files. Which of the following accurately describes the types of data stored?

Accepted Answer

D. Structured, semi-structured, and unstructured data are stored.. The company stores product inventory data in a SQL database, which enforces a fixed schema (tables, rows, columns) and is therefore structured data. Customer reviews stored as JSON files are semi-structured because they have a flexible schema (key-value pairs) but no rigid table structure. Product images as JPEG files are unstructured because they lack any predefined schema or organization. Option D correctly identifies that all three data types are present.

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A. Only structured data is stored because the SQL database contains the primary records.

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B. Only semi-structured and unstructured data is stored because JSON and images are not purely structured.

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C. Only unstructured data is stored because images have no predefined schema.