DP-900 Practice Test 9 — 10 Questions

Question 1

A social media application allows users to post updates and like posts. After a user clicks the like button, the like count must update immediately and be exactly the same for all users viewing the post. Which data consistency model best fits this requirement?

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

Accepted Answer

Strong consistency. Strong consistency ensures that after a write operation (like clicking the like button) completes, any subsequent read operation returns the most recent write. This guarantees that all users viewing the post see the exact same, up-to-date like count immediately. This is required for the social media scenario where the like count must be identical for all viewers without any delay.

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Eventual consistency

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Session consistency

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Bounded staleness consistency

Question 2

Your company, Contoso Ltd., operates a global e-commerce platform. The data engineering team ingests over 10 TB of raw clickstream data daily into Azure Data Lake Storage Gen2. The data is partitioned by date and hour. Business analysts need to query this data using Azure Synapse Serverless SQL to generate daily sales reports. However, the reports are taking over 30 minutes to run, and the team needs to improve query performance without moving data to a dedicated SQL pool. You are asked to recommend a solution. Which action should you take?

Accepted Answer

Create external tables using a partition elimination strategy and ensure the data is partitioned by date.. Azure Synapse Serverless SQL can use external tables with partition elimination to skip irrelevant partitions (e.g., date/hour folders) during query execution. This reduces the amount of data scanned, directly improving query performance without moving data. Partition elimination works by filtering on the partition column (e.g., date) in the WHERE clause, allowing the query engine to read only the necessary files.

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Convert the data from JSON to Parquet format and apply Snappy compression.

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Use Azure Data Factory to copy the data into Azure SQL Database and create indexes.

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Create a dedicated SQL pool and distribute the data across 60 distributions.

Question 3

A company uses Azure Synapse Analytics dedicated SQL pool for its data warehouse. Every night, they need to load 500 GB of new sales data from CSV files stored in Azure Data Lake Storage Gen2. The loading process must be automated, scheduled, and include error handling (e.g., skip corrupt rows and log them). Which Azure service should be used to orchestrate this load pipeline?

Accepted Answer

Azure Data Factory. Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service that supports scheduled execution, error handling (e.g., skipping corrupt rows via fault tolerance settings in the Copy activity), and native integration with Azure Data Lake Storage Gen2 and Azure Synapse dedicated SQL pool. ADF can automate the nightly 500 GB load using a trigger, and its mapping data flows or Copy activity can log errors to a separate file or table, meeting the requirement for automated, scheduled, and error-tolerant ingestion.

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Azure Stream Analytics

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Azure HDInsight

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Azure Logic Apps

Question 4

A company stores historical sensor data in Azure Blob Storage. The data is accessed only a few times per year for compliance audits, but when requested, it must be available for reading within 15 minutes. The company wants to minimize storage costs. Which blob access tier should they use?

Accepted Answer

Cool. The Cool tier is the optimal choice because it balances low storage cost with the ability to retrieve data within minutes, meeting the 15-minute availability requirement. Archive would incur a retrieval delay of up to 15 hours, which violates the compliance audit SLA. Hot and Premium tiers are more expensive and unnecessary for data accessed only a few times per year.

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Hot

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Archive

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Premium

Question 5

A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?

Accepted Answer

Azure Data Factory with a Mapping Data Flow.. Azure Data Factory with a Mapping Data Flow is correct because it provides a code-free, visual data transformation environment that can run on a scheduled trigger (every hour), handle schema drift automatically via schema drift options in Mapping Data Flows, and process JSON files from Azure Blob Storage into a structured format for reporting. This meets all requirements: scheduled execution, transformation, and schema evolution without manual intervention.

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Azure Stream Analytics with a reference data input.

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Azure SQL Database with a stored procedure.

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Azure Logic Apps with a JSON parser.

Question 6

A gaming company stores player scores in Azure Cosmos DB using the NoSQL API. Each document contains: PlayerID (unique to player), GameID, Score, Timestamp. The most common query is: 'Retrieve all scores for a specific GameID, ordered by Score descending.' Which property should be chosen as the partition key to minimize Request Unit (RU) consumption?

Accepted Answer

GameID. GameID is the correct partition key because the most common query filters on GameID, and Cosmos DB routes each query to the physical partition(s) containing that GameID's data. Using GameID ensures the query touches only the relevant partition(s), minimizing RU consumption by avoiding cross-partition fan-out. A partition key that matches the filter predicate is essential for efficient, single-partition queries.

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PlayerID

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Score

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Timestamp

Question 7

A company runs an online booking system on Azure SQL Database. The system handles many concurrent transactions (OLTP). The business team runs complex reporting queries on the same database during business hours, which slows down the booking transactions. The company needs a solution to separate the analytical workload from the transactional workload without duplicating data manually. Which Azure SQL Database feature should they use?

Accepted Answer

Read Scale-out (readable secondary replica). Read Scale-out (readable secondary replica) is the correct choice because it allows the company to offload complex reporting queries to a read-only replica of the primary database, thereby isolating the analytical workload from the OLTP transactions. This feature is built into Azure SQL Database and does not require manual data duplication or ETL processes, directly addressing the requirement to separate workloads without manual effort.

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Active Geo-Replication

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Elastic pools

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Hyperscale service tier

Question 8

A company uses Azure SQL Database for an HR system. The Employees table has a clustered index on EmployeeID. Queries frequently filter on DepartmentID and LastName and also retrieve the Salary column. The table contains over a million rows. Which index strategy will most improve query performance for these filters?

Accepted Answer

A: Create a nonclustered index on (DepartmentID, LastName) INCLUDE (Salary). It creates a covering nonclustered index on the filter columns (DepartmentID, LastName) and includes the Salary column as an included column. This allows the query to be fully satisfied by scanning only the nonclustered index pages, avoiding key lookups to the clustered index. The order of columns in the index key matches the query filter pattern, maximizing seek efficiency.

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B: Create a nonclustered index on LastName only

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C: Create a nonclustered index on DepartmentID and another nonclustered index on LastName

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D: Change the clustered index to (DepartmentID, LastName)

Question 9

A company stores customer orders. Each order has a unique order ID, customer ID, a list of items (each item contains product ID, quantity, and price), and an order date. They frequently query orders by customer ID and also need to filter by order date ranges. The data volume is high and schema flexibility is desired because items can vary in structure. Which type of data store is best suited for this scenario?

Accepted Answer

Document database. A document database (e.g., Azure Cosmos DB for NoSQL) is ideal because it stores each order as a self-contained JSON document, allowing the items array to vary in structure per order (schema flexibility). It supports efficient queries by customer ID (using a partition key) and filtering by order date ranges (using indexing on the date field), while handling high data volumes with horizontal scaling.

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Relational database

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Key-value store

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Graph database

Question 10

A retail company wants to analyze years of historical sales data stored as CSV files in Azure Blob Storage. The analytics solution must be serverless, allow T-SQL queries without managing infrastructure, and integrate directly with Power BI. Which Azure service should the company use?

Accepted Answer

Azure Synapse Analytics serverless SQL pool. Azure Synapse Analytics serverless SQL pool is the correct choice because it provides a serverless, on-demand query service that can directly query CSV files stored in Azure Blob Storage using T-SQL without requiring any infrastructure management. It integrates natively with Power BI via the T-SQL endpoint, enabling direct data visualization from the queried files.

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Azure SQL Database

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Azure Cosmos DB

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Azure Analysis Services