DP-900 Practice Test 45 — 15 Questions

Question 1

A retail company captures real-time clickstream data from its website. They need to store this data for immediate analysis using KQL. Which Azure service should they use?

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is optimized for interactive analytics on large volumes of streaming and high-velocity data, supporting Kusto Query Language (KQL) for real-time queries. It ingests clickstream data with low latency and provides immediate analysis capabilities, making it the correct choice for this scenario.

Answer

Azure Stream Analytics

Answer

Azure Cosmos DB

Answer

Azure SQL Database

Question 2

Which THREE components are part of a typical modern data warehouse architecture on Azure? (Choose three.)

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is a core component of a modern data warehouse architecture on Azure because it provides a unified analytics platform that combines big data and data warehousing capabilities. It enables T-SQL-based querying of both relational and non-relational data, integrating with Azure Data Lake Storage Gen2 for scalable storage and Azure Data Factory for orchestration.

Answer

Power BI

Answer

Azure Stream Analytics

Question 3

A retail company processes historical sales data in a nightly batch job that loads aggregated reports into a data warehouse. Additionally, the company analyzes live customer interactions from their website to provide real-time product recommendations. Which pair of terms correctly describes these two data processing approaches?

Accepted Answer

Batch processing and streaming processing. The nightly batch job that loads aggregated reports into a data warehouse is a classic example of batch processing, where data is processed in large, scheduled chunks. The real-time analysis of live customer interactions for product recommendations is streaming processing, which handles data continuously as it arrives. Option B correctly pairs these two distinct processing paradigms.

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OLTP and OLAP

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Structured data and unstructured data

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Relational and NoSQL

Question 4

A manufacturing company installs temperature sensors in a factory. Sensor data is streamed to Azure Event Hubs. The company needs to detect when the average temperature of any sensor exceeds 100°F over a 5-minute sliding window and then send an alert. Which Azure service should be used for this real-time stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, including the ability to define a 5-minute sliding window over sensor data from Event Hubs, compute the average temperature per sensor, and trigger an alert when the threshold of 100°F is exceeded. It natively integrates with Event Hubs as an input and supports temporal window functions like TumblingWindow, HoppingWindow, and SlidingWindow for exactly this kind of time-based aggregation.

Answer

Azure Functions

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Azure SQL Database

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Azure Logic Apps

Question 5

A healthcare provider stores patient admission data in a relational database table with columns for PatientID, Name, and AdmissionDate. Progress notes are stored as free-text documents. Lab results are stored as XML files that contain varying fields depending on the test type. Which of the following correctly categorizes these three data types in order: relational table, progress notes, lab results?

Accepted Answer

Structured, Unstructured, Semi-structured. The relational table with PatientID, Name, and AdmissionDate enforces a fixed schema with defined data types, making it structured data. Progress notes as free-text documents have no predefined structure or schema, classifying them as unstructured data. Lab results in XML files use tags to organize data but allow varying fields per test type, which is the hallmark of semi-structured data. Option A correctly maps these in order: structured, unstructured, semi-structured.

Answer

Structured, Semi-structured, Unstructured

Answer

Semi-structured, Unstructured, Structured

Answer

Unstructured, Structured, Semi-structured

Question 6

A data analyst needs to run interactive SQL queries on a large dataset stored as CSV files in Azure Blob Storage. The analyst wants to explore the data using T-SQL without loading the data into a database. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is correct because it allows you to run interactive T-SQL queries directly against CSV files in Azure Blob Storage without loading the data into a database. It uses a pay-per-query model and leverages the OPENROWSET function to query external data in place, making it ideal for ad-hoc exploration of large datasets.

Answer

Azure SQL Database

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Azure Data Factory

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Azure Stream Analytics

Question 7

A financial services company runs a critical application on Azure SQL Managed Instance. They need to ensure that a recent transaction can be recovered within 15 minutes of a user error. Which feature should they configure?

Accepted Answer

Point-in-time restore (PITR). Point-in-time restore (PITR) is the correct feature because it allows you to restore an Azure SQL Managed Instance to a specific point in time within the retention period (default 7 days, configurable up to 35 days). This directly addresses the requirement to recover a recent transaction after a user error, such as an accidental data modification or deletion, within 15 minutes. PITR creates a new database from automated backups, enabling precise recovery to the moment just before the error occurred.

Answer

Geo-restore

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Automatic failover groups

Answer

Long-term retention (LTR)

Question 8

A social media startup stores user profile data, posts, and comments in Azure Cosmos DB. They notice that the logical partition size for a popular user's profile is growing beyond 20 GB, causing performance issues. The current partition key is 'userId'. Which action should they take to solve this?

Accepted Answer

Change the partition key to a synthetic key combining userId and postId. A is correct because the logical partition size limit in Azure Cosmos DB is 20 GB. By using a synthetic partition key that combines 'userId' and 'postId', you distribute the data for the popular user across multiple logical partitions, preventing any single partition from exceeding the 20 GB limit and resolving the performance bottleneck.

Answer

Increase the RU/s

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Split the container into multiple containers by userId range

Answer

Use a different API like MongoDB

Question 9

A social media application stores user posts in Azure Cosmos DB. Each document contains fields: PostID (unique), UserID, Timestamp, Content, LikesCount. The most common query retrieves all posts by a specific UserID ordered by Timestamp descending. Which partition key and indexing strategy minimizes Request Unit (RU) consumption?

Accepted Answer

Partition key: UserID; Index: range on Timestamp. The query filters on UserID, so setting UserID as the partition key ensures all posts for a user are in the same physical partition, avoiding cross-partition queries. Adding a range index on Timestamp allows efficient sorting without additional RU overhead, as Cosmos DB can use the index to return results in descending order directly.

Answer

Partition key: PostID; Index: range on Timestamp

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Partition key: Timestamp; Index: range on UserID

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Partition key: UserID; Index: composite on (UserID, PostID)

Question 10

Your data engineering team is designing a data pipeline that ingests data from multiple sources into Azure Data Lake Storage Gen2. The data must be cataloged in Azure Purview for discoverability. Which approach ensures that the data lineage is automatically captured?

Accepted Answer

Use Azure Data Factory with Purview integration enabled to copy data.. Azure Data Factory's native Purview integration automatically captures lineage metadata during data copy activities. When enabled, Data Factory pushes runtime lineage information (source, sink, transformation steps) directly to Purview without manual intervention, ensuring complete and accurate data provenance.

Answer

Use Azure Data Factory to copy data and manually register the datasets in Purview.

Answer

Use Azure Databricks to write data and call Purview's Atlas API to update lineage.

Answer

Schedule Purview scans on the data lake after data ingestion.

Question 11

A company is migrating an on-premises SQL Server database to Azure SQL Database. The database currently uses SQL Server Agent jobs for nightly ETL processes. Which Azure service should the company use to replace these jobs?

Accepted Answer

Elastic Database Jobs. Elastic Database Jobs in Azure SQL Database can replace SQL Server Agent jobs for scheduling T-SQL scripts across multiple databases. Option A is incorrect because Azure Automation is not designed for T-SQL job scheduling; it is used for automating Azure management tasks. Option B is incorrect because Azure Logic Apps is for workflow automation and integration, not for direct T-SQL job scheduling. Option D is incorrect because Azure Data Factory is primarily for data integration and orchestration, not for scheduling ad-hoc T-SQL jobs.

Answer

Azure Automation

Answer

Azure Logic Apps

Answer

Azure Data Factory

Question 12

Which TWO data storage types are classified as structured data in Azure? (Choose two.)

Accepted Answer

Azure SQL Managed Instance. Azure SQL Managed Instance is a fully managed SQL Server database engine in Azure, which stores data in a relational schema with predefined tables, columns, and data types. This structured format enforces a rigid schema, making it a classic example of structured data storage in Azure.

Answer

Azure Cosmos DB

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Azure Data Lake Storage

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Azure Blob Storage

Question 13

Which TWO Azure services are primarily used for batch processing of large volumes of data? (Choose two.)

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is correct because it provides a cloud-based data warehousing and analytics service that uses massively parallel processing (MPP) to run complex queries and batch processing jobs over large datasets, often using PolyBase or T-SQL to transform and load data in bulk. Azure Databricks is correct because it is an Apache Spark-based analytics platform optimized for batch processing, allowing users to run distributed data processing jobs (e.g., ETL, data transformation) across large volumes of data using DataFrames and RDDs in a cluster environment.

Answer

Azure SQL Database

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Azure Stream Analytics

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Azure Data Lake Storage

Question 14

A business analyst needs to create interactive visualizations and share dashboards with colleagues using data stored in an Azure Synapse Analytics dedicated SQL pool. Which tool should the analyst use?

Accepted Answer

Power BI Desktop. Power BI Desktop is the correct tool because it is designed for creating interactive visualizations and dashboards, and it can connect directly to Azure Synapse Analytics dedicated SQL pools via the built-in Azure Synapse Analytics connector. This allows the business analyst to build reports and share them with colleagues through the Power BI service, meeting the requirement for interactive dashboards and collaboration.

Answer

Azure Data Studio

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SQL Server Management Studio (SSMS)

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Azure Machine Learning Studio

Question 15

Drag and drop the steps to ingest data into Azure Data Explorer (ADX) in the correct order.

Accepted Answer

Create the target table, then define mapping, then execute the ingestion, then verify.. Ingestion involves creating the target table, defining mapping, executing the ingestion, and verifying.

Answer

Define mapping, then create the target table, then execute the ingestion, then verify.

Answer

Execute the ingestion, then verify, then create the target table, then define mapping.

Answer

Create the target table, then execute the ingestion, then verify, then define mapping.