DP-900 Practice Test 43 — 15 Questions

Question 1

A retail company runs a nightly process that reads all sales transactions from the previous day, aggregates them by product category and store location, and writes the summary results into a data warehouse for reporting. Which type of data processing workload best describes this nightly process?

Accepted Answer

Batch processing. The nightly process reads all sales transactions from the previous day, aggregates them, and writes summary results into a data warehouse. This is a classic batch processing workload because data is collected over a period (the entire previous day), processed in a single offline job, and the output is stored for later reporting. Batch processing is ideal for high-volume, non-real-time transformations like nightly ETL (Extract, Transform, Load) jobs.

Answer

Online Transaction Processing (OLTP)

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Stream processing

Answer

Data warehousing

Question 2

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 3

A marketing company ingests streaming data from social media feeds into Azure Event Hubs. They want to perform real-time sentiment analysis on the data and store the results in Azure SQL Database for immediate dashboarding. They also need to aggregate the raw data over longer time windows and store it in Azure Data Lake Storage for historical trend analysis. Which combination of Azure services should they use for the two processing paths?

Accepted Answer

Azure Stream Analytics for real-time analysis and Azure Data Factory for batch aggregation. Azure Stream Analytics is ideal for real-time sentiment analysis on streaming data from Event Hubs, as it can process data in-motion with low latency and output directly to Azure SQL Database for immediate dashboarding. Azure Data Factory is the correct choice for batch aggregation over longer time windows, as it can orchestrate and execute periodic data movement and transformation jobs to load aggregated data into Azure Data Lake Storage for historical analysis.

Answer

Azure Databricks for both real-time analysis and batch aggregation

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Azure Stream Analytics for both real-time analysis and batch aggregation

Answer

Azure Data Factory for real-time analysis and Azure Databricks for batch aggregation

Question 4

A retail chain collects daily sales data from hundreds of stores. The data is stored as CSV files in Azure Data Lake Storage Gen2. The analytics team needs to run complex SQL queries that join sales data with product dimensions and aggregate results across petabytes of data. Queries must return results within seconds. Which Azure service is best suited for this analytical workload?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics (formerly SQL Data Warehouse) is the correct choice because it is a distributed query engine designed for petabyte-scale data warehousing. It uses Massively Parallel Processing (MPP) to distribute data across compute nodes, enabling complex SQL joins and aggregations on data stored in Azure Data Lake Storage Gen2 to return results in seconds via its SQL pool or serverless SQL endpoint.

Answer

Azure SQL Database

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Azure Analysis Services

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Azure HDInsight

Question 5

Which TWO are valid deployment options for Azure SQL?

Accepted Answer

Azure SQL Managed Instance. Options A and D are correct. Azure SQL Database and Azure SQL Managed Instance are deployment options. Options B and C are wrong because they are other Azure services. Option E is wrong because Azure Synapse Dedicated SQL Pool is a separate service for data warehousing.

Answer

Azure Cosmos DB

Answer

Azure Database for MariaDB

Answer

Azure Synapse Analytics Dedicated SQL Pool

Question 6

A logistics company uses Azure Synapse Analytics dedicated SQL pool to analyze billions of shipment records. The table 'Shipments' is 10 TB and hash-distributed on 'ShipmentID'. Analysts frequently run queries that filter on 'WarehouseID' and aggregate by 'Region'. These queries are slow because they cause data movement (shuffle) across distributions. Which table design change will most improve query performance for these analytical workloads?

Accepted Answer

Change distribution to hash on 'WarehouseID'. D is correct because hash-distributing the 'Shipments' table on 'WarehouseID' ensures that all rows for a given warehouse are co-located on the same distribution node. This eliminates the need for data movement (shuffle) when queries filter on 'WarehouseID' and aggregate by 'Region', as the aggregation can be performed locally on each distribution without redistributing data across nodes.

Answer

Change distribution to replicated table

Answer

Change distribution to round-robin

Answer

Create a columnstore index

Question 7

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.

Answer

Azure SQL Database

Answer

Azure Cosmos DB

Answer

Azure Analysis Services

Question 8

A retail company collects sales data from multiple stores. Data is ingested into Azure Data Lake Storage Gen2 as CSV files. The data team needs to run ad-hoc SQL queries on this data without moving it, and they want to pay only for the amount of data processed. They also need to integrate with Power BI for visualization. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool (option D) is correct because it allows querying data directly from Azure Data Lake Storage Gen2 using T-SQL without moving the data, and it uses a pay-per-query model where you are billed only for the amount of data processed. It also integrates seamlessly with Power BI for visualization, making it ideal for ad-hoc SQL queries on CSV files.

Answer

Azure Synapse Analytics dedicated SQL pool

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Azure SQL Database

Answer

Azure Data Lake Analytics

Question 9

A manufacturer collects sensor data from thousands of IoT devices every second. The data is ingested into Azure Event Hubs and then needs to be stored for historical analysis. The analytics team will run complex aggregations and time-series queries over petabytes of data, expecting fast results even with large scans. Which Azure service should be used as the analytical data store?

Accepted Answer

Azure Synapse Analytics dedicated SQL pool. Azure Synapse Analytics dedicated SQL pool is the correct choice because it is a massively parallel processing (MPP) engine designed for petabyte-scale data warehousing. It can run complex aggregations and time-series queries with fast results by distributing data across 60 distributions and using columnstore indexes for high compression and scan efficiency.

Answer

Azure Data Lake Storage Gen2

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Azure SQL Database

Answer

Azure Cosmos DB

Question 10

A manufacturing company ingests real-time sensor data from factory equipment via Azure Event Hubs. The data is a continuous stream of measurements (sensorId, timestamp, value). Additionally, historical maintenance records are stored as CSV files in Azure Data Lake Storage Gen2. The operations team needs to join the streaming data with the historical records in near real-time to detect anomalies. They also need to run complex T-SQL queries on the combined dataset for ad-hoc analysis. Which Azure service should they use as the primary analytics platform?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest real-time streaming data from Azure Event Hubs via its built-in pipeline or Spark Structured Streaming, and simultaneously query historical CSV files in Azure Data Lake Storage Gen2 using serverless SQL or dedicated SQL pools. This allows the operations team to join streaming and batch data in near real-time for anomaly detection, and also run complex T-SQL queries for ad-hoc analysis, all within a single service.

Answer

Azure Stream Analytics

Answer

Azure Data Factory

Answer

Azure HDInsight with Spark

Question 11

A retail company runs a nightly job that reads all sales transactions from the previous day from an operational database, aggregates them by product category and store location, and writes the summary results into a data warehouse for reporting. Which type of data processing workload does this nightly job represent?

Accepted Answer

B. Batch processing. The nightly job processes data in discrete, scheduled batches—reading all sales transactions from the previous day, aggregating them, and writing results to a data warehouse. This is the classic definition of batch processing, where data is collected over a period and processed together in a single job run. In Azure, this workload maps to services like Azure Data Factory or Azure Synapse Pipelines executing scheduled pipelines.

Answer

A. Real-time processing

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C. Stream processing

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D. Transactional processing

Question 12

A retail company analyzes customer purchase patterns. Every night, they run a batch job that aggregates millions of transactions from the past day into summary tables for reporting. Which type of data processing workload best describes this nightly job?

Accepted Answer

Batch processing. This nightly job processes a large volume of transactions accumulated over the past day in a single, scheduled run, which is the defining characteristic of batch processing. In Azure, this workload would typically be implemented using Azure Synapse Analytics or Azure Data Factory to orchestrate the aggregation of millions of rows into summary tables for reporting, without requiring immediate output.

Answer

Real-time processing

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Streaming processing

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Transactional processing

Question 13

A retail company needs to analyze sales transactions as they occur to detect fraud patterns and immediately block suspicious orders. They also need to run daily batch reports on historical sales data. Which combination of Azure services should they use to meet both real-time and batch processing requirements?

Accepted Answer

Azure Stream Analytics for real-time processing and Azure Synapse Analytics for batch analytics. Azure Stream Analytics is purpose-built for real-time data streaming and can process sales transactions as they occur to detect fraud patterns and block suspicious orders immediately. Azure Synapse Analytics provides a unified analytics platform that can run large-scale batch queries on historical sales data for daily reports, making this combination ideal for both real-time and batch processing needs.

Answer

Azure Data Factory for both real-time and batch processing

Answer

Azure Logic Apps for real-time processing and Azure Synapse Analytics for batch analytics

Answer

Azure Stream Analytics for both real-time and batch processing

Question 14

You need to store JSON documents that are frequently read and written by a web application. Each document is about 10 KB. The solution must provide low-latency access globally. Which Azure data store should you use?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB is a globally distributed NoSQL database that supports JSON documents and provides low-latency access. Option A is wrong because Blob Storage is object storage, not optimized for individual document reads/writes. Option B is wrong because Table Storage is for key-value data. Option D is wrong because SQL Database is relational.

Answer

Azure Blob Storage

Answer

Azure SQL Database

Answer

Azure Table Storage

Question 15

A marketing company stores years of historical campaign data in Azure Data Lake Storage Gen2 as Parquet files. Data analysts need to run complex SQL queries over this data to identify trends, and they want to visualize results in Power BI dashboards. The company wants to avoid moving data into a separate database to minimize duplication and latency. Which Azure service should they use to query the data directly in the data lake?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it allows you to run T-SQL queries directly over Parquet files in Azure Data Lake Storage Gen2 without moving or copying the data. It uses a pay-per-query model and supports standard SQL syntax, making it ideal for analysts who need to query historical campaign data and visualize results in Power BI with minimal latency.

Answer

Azure Data Factory

Answer

Azure Databricks

Answer

Azure HDInsight