DP-900 Describe an analytics workload on Azure Practice Test 6 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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 6

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 7

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 8

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 9

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 10

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.

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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

Question 11

A retail company ingests daily sales data from multiple stores as CSV files stored in Azure Blob Storage. The data must be cleaned and transformed using Spark, then loaded into Azure Synapse Analytics for large-scale reporting. The pipeline must run on a schedule, handle failures with retries, and minimize manual intervention. Which combination of Azure services should they use to orchestrate and execute this pipeline?

Accepted Answer

Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.. Azure Data Factory provides the orchestration and scheduling layer, Azure Databricks executes the Spark-based cleaning and transformation, and Azure Synapse Analytics serves as the target data warehouse for large-scale reporting. This combination supports retry policies for failure handling and minimizes manual intervention through automated pipeline execution.

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Azure Stream Analytics, Azure Data Lake Storage, and Power BI.

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Azure Functions, Azure SQL Database, and Azure Analysis Services.

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Azure Logic Apps, Azure HDInsight, and Azure Cosmos DB.

Question 12

A retail company needs to analyze clickstream data from their website in real time to detect fraudulent activity and also run complex historical queries on months of data to identify shopping trends. They want a single service that can handle both streaming and batch analytics using a unified query language, minimizing data movement. Which Azure service should they use?

Accepted Answer

Azure Data Explorer. Azure Data Explorer (ADX) is designed for real-time analytics on streaming data and can also handle complex historical queries over large volumes of data using the Kusto Query Language (KQL). It minimizes data movement by ingesting streaming data directly and storing it in a columnar format optimized for both real-time and batch queries, making it the ideal single service for this scenario.

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Azure Stream Analytics

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Azure Synapse Analytics

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Azure HDInsight

Question 13

A data engineering team needs to transform raw clickstream data stored as Parquet files in Azure Data Lake Storage Gen2. They want to use standard T-SQL queries to perform transformations and aggregations. The team prefers a serverless option to avoid provisioning and managing dedicated compute resources. Which Azure service should they use?

Accepted Answer

Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is correct because it allows querying Parquet files in Azure Data Lake Storage Gen2 using standard T-SQL without provisioning any dedicated compute resources. It automatically scales compute based on query demand, making it ideal for ad-hoc transformations and aggregations on raw data with a serverless, pay-per-query model.

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Azure Synapse Dedicated SQL pool

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Azure Databricks

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Azure HDInsight

Question 14

A manufacturing company uses IoT sensors to collect temperature and vibration data from machinery. They need to analyze the streaming data in real time to detect anomalies and trigger alerts. Additionally, they need to run complex historical queries on months of sensor data to identify equipment failure patterns. They want a single Azure service that can handle both real-time stream processing and large-scale batch analytics using a unified query language, minimizing the need for separate technologies. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified platform that combines both real-time stream processing (via Synapse Pipelines and Spark Streaming) and large-scale batch analytics (via Synapse SQL and Spark) using a single query language (T-SQL or Spark SQL). This eliminates the need for separate technologies, directly addressing the requirement for a single service to handle both streaming and historical batch analysis.

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Azure Stream Analytics

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Azure Data Lake Storage Gen2

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Azure HDInsight

Question 15

A company wants to build a modern data warehouse using a lakehouse architecture. They need to store raw data in its native format (e.g., CSV, JSON, Parquet) and also support BI reporting on curated, transformed data. They want to use a single storage layer for both raw and curated data. Which Azure service should they use as the core storage layer?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct choice because it provides a single, unified storage layer that can store raw data in its native format (CSV, JSON, Parquet) in a hierarchical namespace, while also serving as the foundation for curated, transformed data used in BI reporting. It combines the scalability and cost-effectiveness of Azure Blob Storage with the file system semantics and ACLs needed for analytics workloads, making it the ideal core storage layer for a lakehouse architecture.

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Azure SQL Database

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Azure Synapse Analytics

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Azure Cosmos DB