DP-900 Describe an analytics workload on Azure Practice Test 3 — 15 Questions

Question 1

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 2

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 3

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

Question 4

A retail company receives a continuous stream of customer orders from their website via Azure Event Hubs. They also receive daily inventory updates from suppliers as CSV files uploaded to Azure Blob Storage. The company needs to calculate real-time order fulfillment availability by joining the streaming orders with the latest inventory snapshot. Additionally, they generate nightly sales reports from historical order data. 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, allowing you to join streaming data from Event Hubs with static or reference data (like the latest inventory snapshot from Blob Storage) using SQL-like queries. This enables the calculation of real-time order fulfillment availability as orders arrive, which is the core requirement.

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Azure Data Factory

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Azure Databricks

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Azure Synapse Pipelines

Question 5

A company is designing an enterprise analytics solution. They store raw data in its original format in a scalable repository, apply schema and transformations at read time, and also maintain a curated layer that enforces ACID transactions for data reliability. This architecture combines the flexibility of a data lake with the reliability of a data warehouse. Which term best describes this modern data architecture?

Accepted Answer

Data lakehouse. The data lakehouse architecture combines the flexibility of a data lake (storing raw data in its original format in a scalable repository) with the reliability of a data warehouse (enforcing ACID transactions in a curated layer). This allows schema-on-read transformations while maintaining data integrity, making it the correct term for the described design.

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Data mart

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Operational database

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Data pipeline

Question 6

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

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics provides the serverless SQL pool capability that allows you to query data directly in Azure Data Lake Storage Gen2 using T-SQL without moving or copying the data. This enables business analysts to run complex SQL queries over Parquet files in the lake and connect the results to Power BI for visualization, minimizing storage costs and latency by avoiding a separate database.

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Azure SQL Database

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Azure Data Factory

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Azure Analysis Services

Question 7

A financial services company needs to build a data pipeline that ingests daily transaction files from multiple sources. The pipeline must perform data quality checks, transform data using complex business logic, and load it into Azure Synapse Analytics. The transformations involve conditional branching (e.g., if a transaction amount exceeds a threshold, apply additional validation). The company wants to minimize coding effort and prefers a visual, configuration-based approach. Which Azure service should they use as the primary orchestration and transformation engine?

Accepted Answer

Azure Data Factory with Data Flows. Azure Data Factory with Data Flows is the correct choice because it provides a visual, configuration-based interface for both orchestration and transformation, including support for complex business logic like conditional branching (e.g., via conditional split transformations). It natively integrates with Azure Synapse Analytics for loading transformed data, minimizing coding effort compared to code-heavy alternatives.

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Azure Databricks with notebooks

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Azure Stream Analytics

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Azure Logic Apps

Question 8

A retail company ingests clickstream data from its e-commerce website into Azure Event Hubs. They need to detect customer journey patterns in real time within seconds and also prepare aggregated data for daily trend reports stored in Azure Data Lake Storage Gen2. The real-time processing must handle high throughput and support complex temporal queries like sessionization. The daily aggregation should be cost-effective and use serverless compute. Which combination of Azure services should they use?

Accepted Answer

Azure Stream Analytics for real-time processing and Azure Data Factory for daily batch aggregation. Azure Stream Analytics is ideal for real-time processing of high-throughput clickstream data from Event Hubs, supporting complex temporal queries like sessionization with low latency (seconds). Azure Data Factory provides cost-effective, serverless orchestration for daily batch aggregation, efficiently moving and transforming data to Azure Data Lake Storage Gen2 without managing infrastructure.

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Azure Functions for real-time processing and Azure Databricks for daily batch aggregation

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Azure Stream Analytics for real-time processing and Azure Batch for daily batch aggregation

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Azure Data Lake Analytics for real-time processing and Azure Data Factory for daily batch aggregation

Question 9

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.

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Azure Data Factory

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Azure Databricks

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Azure HDInsight

Question 10

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 11

A manufacturing company deploys IoT sensors on equipment in a factory. They need to monitor sensor data in real time to detect anomalies and trigger immediate alerts. They also need to store years of historical sensor data for monthly capacity planning reports that involve complex aggregations. The company wants a cost-effective solution that minimizes data movement between storage and compute. Which combination of Azure services should they use for real-time processing and historical batch analytics?

Accepted Answer

A. Azure Stream Analytics for real-time processing, Azure Data Lake Storage Gen2 for historical storage, and Azure Synapse Analytics for batch queries.. Azure Stream Analytics is purpose-built for real-time processing of streaming data from IoT sensors, enabling immediate anomaly detection and alerting. Azure Data Lake Storage Gen2 provides cost-effective, scalable storage for years of historical sensor data, while Azure Synapse Analytics (formerly SQL Data Warehouse) can run complex aggregations directly against that data without moving it, minimizing data movement and cost.

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B. Azure Data Factory for real-time processing, Azure Cosmos DB for historical storage, and Power BI for batch queries.

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C. Azure Functions for real-time processing, Azure Table Storage for historical storage, and Azure Analysis Services for batch queries.

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D. Azure Event Hubs for real-time processing, Azure SQL Database for historical storage, and Azure Machine Learning for batch queries.

Question 12

A retail company needs to build an analytics pipeline on Azure. They ingest sales data from multiple store systems and an online e-commerce platform. The data must be cleaned, transformed, and loaded into a data warehouse for reporting. The company wants to use a modern ELT (Extract, Load, Transform) approach where raw data is stored first and then transformed. Order the following steps in the correct sequence for this pipeline. (Drag the steps into the correct order.)

Accepted Answer

Ingest data from sources using Azure Data Factory.. ELT approach first ingests raw data to storage, loads it into the warehouse, then transforms it before creating reporting structures.

Question 13

A company stores terabytes of historical sales data as Parquet files in Azure Data Lake Storage Gen2. Business analysts need to run ad-hoc SQL queries that involve complex joins and aggregations over this data. They want to avoid provisioning a dedicated cluster or moving data into a separate database. The queries must be executed using standard T-SQL syntax. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics serverless SQL pool. Azure Synapse Analytics serverless SQL pool (B) is the correct choice because it allows you to query Parquet files directly in Azure Data Lake Storage Gen2 using standard T-SQL syntax without provisioning any dedicated cluster or moving data. It automatically scales compute resources to handle complex joins and aggregations on terabytes of data, charging only for the data processed. This matches the requirement for ad-hoc, serverless querying with familiar T-SQL.

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Azure Synapse Analytics dedicated SQL pool

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Azure Databricks

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Azure HDInsight

Question 14

A telecommunications company needs to analyze call detail records (CDRs) to detect fraud patterns and minimize revenue leakage. The data arrives as a continuous stream from network switches and must be queried within seconds of ingestion to flag suspicious activity. The analysts also need to run interactive ad-hoc queries over the last 90 days of CDR data using a Kusto query language. Which Azure service should they use as the primary data store and analytics engine?

Accepted Answer

Azure Data Explorer. Azure Data Explorer is optimized for high-velocity telemetry data like CDRs, supporting ingestion of continuous streams with sub-second query latency. Its native Kusto Query Language (KQL) enables both real-time fraud detection and interactive ad-hoc queries over large time windows (e.g., 90 days) without pre-aggregation or indexing overhead.

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Azure Synapse Analytics

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Azure Stream Analytics

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Azure Analysis Services

Question 15

A company uses Azure Synapse Analytics dedicated SQL pool for large-scale data warehousing. They have a fact table with billions of rows and frequently run queries that filter by a date range and join with a product dimension table. Which table distribution and partitioning strategy will minimize data movement and improve query performance?

Accepted Answer

Hash-distribute on ProductID with partitioning on Date. Hash-distributing the fact table on ProductID ensures that rows for the same product are co-located on the same distribution, minimizing data movement when joining with the product dimension table. Partitioning on Date allows partition elimination for date-range filters, reducing the amount of data scanned. This combination directly addresses the query pattern of date-range filtering and product joins.

Answer

Round-robin distribution with no partitioning

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Replicate the fact table on all distributions and partition on ProductID

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Hash-distribute on Date with partitioning on ProductID