DP-900 Describe an analytics workload on Azure Practice Test 4 — 15 Questions

Question 1

A manufacturing company connects thousands of IoT sensors on an assembly line, each sending telemetry data every second. The data volume is terabyte-scale per day. The company needs to analyze the sensor data in near real-time to detect anomalies (e.g., temperature spikes) and also allow data scientists to run interactive ad-hoc queries on the historical data to find patterns. They prefer using a query language similar to SQL. Which Azure service should they choose?

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

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is designed for high-velocity telemetry data, ingesting terabytes per day from IoT sensors with sub-second latency. It supports Kusto Query Language (KQL), which is SQL-like and optimized for time-series analysis, anomaly detection, and interactive ad-hoc queries on both real-time and historical data. This makes it the ideal choice for the described scenario.

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Azure Stream Analytics

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Azure Synapse Analytics dedicated SQL pool

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Azure Databricks with Structured Streaming

Question 2

A financial services company stores petabytes of transaction data in Parquet format in Azure Data Lake Storage Gen2. Data analysts need to run complex SQL queries that join multiple large tables and aggregate billions of rows, with results expected within seconds. The company wants to use a massively parallel processing (MPP) engine that supports T-SQL and can be paused to reduce costs during off-hours. They also need native integration with Azure Data Factory and Power BI. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics (formerly SQL DW) is the correct choice because it provides a massively parallel processing (MPP) engine that distributes data across 60 distributions, enabling complex T-SQL queries on petabyte-scale Parquet data with results in seconds. It supports native T-SQL, can be paused to reduce costs during off-hours, and offers built-in integration with Azure Data Factory and Power BI through its SQL endpoints and linked service connectors.

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Azure HDInsight

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Azure Databricks

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Azure SQL Database

Question 3

A company stores terabytes of web server log data in CSV files in Azure Data Lake Storage Gen2. Data analysts need to run ad-hoc SQL queries on this data to analyze user behavior patterns. The queries are complex, involve joins across multiple files, and the analysts prefer not to move the data into a separate store. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is the correct choice because it allows analysts to run T-SQL queries directly against CSV files stored in Azure Data Lake Storage Gen2 without moving the data. It uses a distributed query engine to process complex joins across multiple files, making it ideal for ad-hoc analytics on large-scale log data.

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Azure Data Factory

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Azure SQL Database

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Azure HDInsight

Question 4

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 5

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.

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Azure Stream Analytics

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Azure Data Factory

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Azure HDInsight with Spark

Question 6

A logistics company ingests real-time GPS data from delivery vehicles via Azure Event Hubs. The data includes vehicle ID, latitude, longitude, and timestamp. The company also has historical route plan data stored as CSV files in Azure Data Lake Storage Gen2. Data analysts need to combine the live stream with the historical data in near real-time to create a dashboard showing if vehicles are on schedule. They also need to run complex T-SQL queries on the combined dataset for ad-hoc reporting. Which Azure service should they use as the primary analytics platform?

Accepted Answer

C: Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest real-time data from Azure Event Hubs via its built-in streaming capabilities (e.g., using Synapse Pipelines or Spark Structured Streaming) and combine it with historical data stored in Azure Data Lake Storage Gen2. It supports complex T-SQL queries through its dedicated SQL pool (formerly SQL Data Warehouse) for ad-hoc reporting, enabling near real-time dashboards and interactive analytics on the combined dataset.

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A: Azure Stream Analytics

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B: Azure Data Lake Analytics

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D: Azure Analysis Services

Question 7

A company uses Azure Data Factory to run a pipeline that copies new orders from an on-premises SQL Server database to Azure Data Lake Storage every hour. After the data is in the data lake, an Azure Databricks notebook transforms it and loads it into Azure Synapse Analytics for reporting. Which type of data processing does the hourly copy operation represent?

Accepted Answer

Batch processing. The hourly copy operation from on-premises SQL Server to Azure Data Lake Storage is a classic batch processing pattern: data is collected over a fixed time interval (1 hour) and processed as a single unit. Azure Data Factory orchestrates this scheduled, non-continuous transfer, which aligns with batch processing's definition of handling data in discrete, periodic chunks rather than in real-time.

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Real-time streaming

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Interactive query

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Transactional processing

Question 8

A manufacturing company ingests a continuous stream of sensor data from thousands of IoT devices into Azure Event Hubs. The company also stores historical equipment maintenance records in Azure SQL Database. The operations team needs to join the streaming sensor data with the historical maintenance records in near real-time to detect anomalies, and data scientists need to run ad-hoc T-SQL queries on the combined dataset for analysis. Which Azure service should they use as the primary analytics platform to meet both requirements?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that can ingest streaming data from Azure Event Hubs via its built-in Spark pools or pipelines, and simultaneously query historical data in Azure SQL Database using T-SQL. This enables near real-time anomaly detection through streaming joins and ad-hoc T-SQL queries for data scientists, all within a single service without needing separate tools.

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Azure Stream Analytics

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Azure Databricks

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Azure Analysis Services

Question 9

A company ingests raw clickstream data as JSON files into Azure Data Lake Storage Gen2. Data scientists need to explore the data interactively using Python notebooks, and the BI team needs to create reports from aggregated datasets derived from this data. The solution must be serverless, scale automatically, and minimize administration. Which Azure service should they choose?

Accepted Answer

B. Azure Databricks. Azure Databricks is correct because it provides a serverless, interactive Apache Spark environment that data scientists can use with Python notebooks for exploratory analysis, and it can produce aggregated datasets for BI reporting. It scales automatically and minimizes administration by managing the cluster lifecycle, making it ideal for ad-hoc data exploration on raw JSON files in Azure Data Lake Storage Gen2.

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A. Azure Synapse Analytics (serverless SQL pool)

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C. Azure HDInsight with Spark

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D. Azure Data Lake Analytics

Question 10

A retail company wants to analyze customer clickstream data in real-time to detect patterns and trigger personalized offers. They also store the raw clickstream data in Azure Data Lake Storage for later batch analysis. Which Azure service should they use for the real-time processing component?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is the correct choice because it is designed for real-time data processing and analytics on streaming data, such as clickstream events. It can ingest data from sources like Azure Event Hubs, apply SQL-like queries to detect patterns, and output results to triggers or storage, all with sub-second latency. This matches the requirement for real-time pattern detection and personalized offer triggering.

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Azure Data Factory

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Azure Batch

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Azure Data Lake Analytics

Question 11

A smart building monitoring company ingests real-time sensor data (temperature, humidity, occupancy) from thousands of IoT devices into Azure Event Hubs. The company also stores historical building blueprints and maintenance records as CSV files in Azure Data Lake Storage Gen2. The engineering team needs to build a dashboard that displays live sensor readings overlaid on building floor plans, and also allows facility managers to run ad-hoc T-SQL queries that combine live sensor data with historical maintenance records. Which Azure service should they use as the primary analytics platform to meet both requirements?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that combines big data and data warehousing. It can ingest real-time streaming data from Azure Event Hubs via its built-in Spark pools or pipelines, and also run ad-hoc T-SQL queries against both the live sensor data (staged in tables) and historical CSV files stored in Azure Data Lake Storage Gen2 using serverless SQL pools. This meets both the real-time dashboard and ad-hoc T-SQL query requirements.

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Azure Stream Analytics

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Azure Databricks

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Azure Analysis Services

Question 12

A financial services company uses Azure Synapse Analytics to process large volumes of transaction data. They have a dedicated SQL pool (formerly SQL DW) that ingests curated, aggregated data nightly from a data lake. Data analysts need to run ad-hoc, exploratory T-SQL queries on raw transaction data stored as Parquet files in Azure Data Lake Storage Gen2. These queries vary widely in complexity and frequency. The company wants to minimize costs for these ad-hoc queries while still using full T-SQL capabilities. Which approach should they recommend?

Accepted Answer

Create a serverless SQL pool endpoint to query the data lake directly.. Serverless SQL pool in Azure Synapse Analytics is designed for ad-hoc, on-demand querying of data lake files (like Parquet) without provisioning or paying for dedicated compute resources. It supports full T-SQL syntax and charges only for the data processed per query, making it cost-effective for exploratory workloads with variable complexity and frequency.

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Use external tables in the dedicated SQL pool to query the data lake directly.

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Load the raw data into the dedicated SQL pool before querying.

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Use Azure Data Explorer to query the data lake.

Question 13

A company receives daily sales data from multiple retail stores as CSV files that are uploaded to Azure Blob Storage. The data must be cleansed, validated, and aggregated before being loaded into Azure Synapse Analytics for reporting. The transformations involve complex business logic and must run reliably every night. The company wants a service that can orchestrate and execute the entire pipeline with minimal development effort. Which Azure service should they use?

Accepted Answer

Azure Data Factory with mapping data flows. Azure Data Factory with mapping data flows is correct because it provides a code-free, visual interface for building complex data transformations (cleansing, validation, aggregation) that can be orchestrated on a schedule. Mapping data flows execute at scale on Azure Databricks clusters without requiring manual Spark code, making it ideal for nightly batch ETL pipelines with minimal development effort.

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Azure Stream Analytics

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Azure Databricks

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Azure Logic Apps

Question 14

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.

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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

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

Question 15

A company receives real-time clickstream data from its website via Azure Event Hubs. They need to detect fraudulent clicks within seconds and also produce daily aggregate reports of visitor statistics for historical analysis. Which combination of Azure services should they use for the real-time detection and the daily aggregation, respectively?

Accepted Answer

Azure Stream Analytics for real-time detection; Azure Data Factory for daily aggregation. Azure Stream Analytics is purpose-built for real-time stream processing, making it ideal for detecting fraudulent clicks within seconds from Event Hubs. Azure Data Factory is a cloud-based ETL service that can orchestrate and execute daily aggregation jobs on historical data, such as producing visitor statistics reports from stored clickstream data.

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Azure Databricks for both real-time detection and daily aggregation

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Azure Synapse Analytics for real-time detection; Azure Blob Storage for daily aggregation

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Azure Functions for real-time detection; Azure SQL Database for daily aggregation