DP-900 Describe an analytics workload on Azure Practice Test 7 — 15 Questions

Question 1

A retail company processes petabytes of sales transaction data stored in Azure Data Lake Storage Gen2. They need to run recurring complex queries that involve large joins and aggregations. The queries must consistently complete within a fixed time window overnight. The company wants predictable performance and costs. Which Azure service should they use?

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

Accepted Answer

Azure Synapse Analytics Dedicated SQL pool. Azure Synapse Analytics Dedicated SQL pool provides reserved, fixed compute resources that ensure predictable performance and cost for recurring complex queries involving large joins and aggregations. It is designed for petabyte-scale data warehousing workloads with consistent SLAs, making it ideal for overnight batch processing within a fixed time window.

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Azure Synapse Analytics Serverless SQL pool

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Azure SQL Database

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Azure Analysis Services

Question 2

A data engineering team is designing a modern data warehouse using Azure Synapse Analytics. They want to follow a lakehouse architecture where raw data is stored in its native format and then processed and curated for reporting. Which component in Azure Synapse Analytics is primarily used to store raw data in its original format without requiring a schema?

Accepted Answer

Data Lake Storage Gen2. Azure Data Lake Storage Gen2 (ADLS Gen2) is the correct component because it provides a scalable, hierarchical file system that can store raw data in any native format (e.g., CSV, JSON, Parquet) without requiring a predefined schema. This aligns with the lakehouse architecture's requirement to ingest and persist raw data as-is before any transformation or curation.

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Dedicated SQL pool

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Serverless SQL pool

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Synapse Pipeline

Question 3

A financial services company runs large-scale analytical queries on a dedicated SQL pool in Azure Synapse Analytics. They notice that during peak hours, complex aggregations consume excessive resources, causing slower queries from other users. They need to ensure that critical management reports always get enough resources and complete within a guaranteed time, while other less important queries do not starve them. Which feature should they implement?

Accepted Answer

Workload management. Workload management in Azure Synapse Analytics allows you to classify, assign resources, and prioritize queries by creating workload groups and classifiers. By configuring a workload group for critical management reports with a higher importance and a guaranteed minimum resource percentage, you ensure those queries always get sufficient resources and complete within a guaranteed time, while less important queries are throttled and cannot starve the critical ones.

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Result-set caching

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Materialized views

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Columnstore index

Question 4

A financial services company uses a dedicated SQL pool in Azure Synapse Analytics to run large-scale analytical queries. During peak hours, complex aggregations consume excessive resources, causing slower performance for other users. The company needs to ensure that critical scheduled management reports always receive guaranteed resources and complete within a predictable timeframe, while less important ad-hoc queries do not interfere. Which feature should they implement to manage query resource allocation?

Accepted Answer

Workload management. Workload management in Azure Synapse Analytics allows you to classify, assign, and govern resources for queries by using workload groups and importance levels. By configuring workload groups, you can guarantee resources for critical scheduled management reports (e.g., assigning high importance) while limiting or deprioritizing less important ad-hoc queries, ensuring predictable completion times during peak hours.

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Result set caching

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Columnstore indexes

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Table distribution

Question 5

A financial services company runs critical end-of-day reports in an Azure Synapse Analytics dedicated SQL pool. These reports require guaranteed resource allocation and must complete within a fixed time window. However, ad-hoc analytical queries from data scientists often consume resources, causing contention and delaying the critical reports. Which feature should the company implement to ensure the critical reports always receive sufficient resources?

Accepted Answer

A. Create a workload group for the critical reports with a high importance setting and assign a minimum percentage of resources.. Workload groups in Azure Synapse Analytics dedicated SQL pool allow you to assign a minimum percentage of resources (e.g., CPU and memory) to a specific workload, ensuring guaranteed resource allocation. By setting high importance for the critical reports, the system prioritizes them over ad-hoc queries, preventing resource contention and ensuring they complete within the fixed time window.

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B. Enable result set caching on all queries to reduce execution time.

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C. Implement materialized views for the aggregations used in the critical reports.

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D. Use hash distribution for the fact tables to improve query parallelism.

Question 6

A large e-commerce company needs to build an analytics solution. They have streaming clickstream data from their website (JSON) and daily sales data from their transactional database (CSV). They need to perform real-time dashboards on clickstream for the current hour, and also run complex historical queries that join sales data with aggregated clickstream data over the past year. They want a single Azure service that can handle both stream processing and batch processing using a unified experience, without moving data between separate systems. Which Azure service should they use?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is the correct choice because it provides a unified experience for both stream processing (via Synapse Pipelines or Spark Structured Streaming) and batch processing (via dedicated SQL pools or serverless SQL), enabling real-time dashboards on clickstream data and complex historical queries joining sales data with aggregated clickstream data without moving data between separate systems.

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Azure Stream Analytics

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Azure Data Factory

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Azure HDInsight

Question 7

A company stores weather sensor data in Azure Data Lake Storage Gen2. Data scientists need to run large-scale transformations and machine learning experiments on this data using Python and Apache Spark. They want to collaborate using shared Jupyter notebooks. Which Azure service should they use for this analytical workload?

Accepted Answer

Azure Synapse Analytics (with Apache Spark pools). Azure Synapse Analytics with Apache Spark pools provides a fully managed Spark environment that supports Python and allows data scientists to run large-scale transformations and machine learning experiments. It integrates directly with Azure Data Lake Storage Gen2 for reading and writing data, and supports collaborative Jupyter notebooks for shared development. This makes it the correct choice for the described analytical workload.

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Azure Stream Analytics

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Azure Analysis Services

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Azure SQL Database

Question 8

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.

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Azure SQL Database

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Azure Cosmos DB

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Azure Analysis Services

Question 9

A business analyst needs to explore and create interactive visualizations of sales data stored in Azure Data Lake Storage Gen2 without writing SQL code. Which Azure service is best suited for this drag-and-drop data exploration?

Accepted Answer

Microsoft Power BI. Microsoft Power BI is the correct choice because it provides a drag-and-drop interface for creating interactive visualizations and exploring data without requiring SQL code. It can directly connect to Azure Data Lake Storage Gen2 using Power Query or the ADLS connector, enabling business analysts to build reports and dashboards through intuitive visual interactions.

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Azure Stream Analytics

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Azure Data Factory

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Azure Databricks

Question 10

A financial institution needs to run complex queries against petabytes of historical trading data stored in Azure Data Lake Storage. The queries must be efficient and use columnar storage format. Which technology should they use to process this data?

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics (formerly SQL Data Warehouse) is the correct choice because it is a cloud-based analytics service designed for petabyte-scale data warehousing. It supports PolyBase to query data directly from Azure Data Lake Storage and uses a columnar storage format (via clustered columnstore indexes) to enable efficient, high-performance analytical queries on massive historical datasets.

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Azure SQL Database

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Azure Cosmos DB

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Azure Table Storage

Question 11

A data engineer needs to query data stored in CSV files in Azure Data Lake Storage Gen2 using T-SQL in Azure Synapse Analytics, without loading the data into the database. Which feature should they use?

Accepted Answer

External tables. External tables in Azure Synapse Analytics allow you to query data stored in files (such as CSV in Azure Data Lake Storage Gen2) using T-SQL without loading the data into the database. They use the PolyBase engine to read the files directly, enabling on-the-fly querying of external data sources.

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Materialized views

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Stored procedures

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Indexed views

Question 12

A data analyst needs to run interactive SQL queries against petabytes of sales data stored in Parquet format in Azure Data Lake Storage Gen2. The analyst wants the fastest query performance for ad-hoc exploration without provisioning or managing any infrastructure. Which Azure service should they use?

Accepted Answer

B. Azure Synapse Serverless SQL pool. Azure Synapse Serverless SQL pool is correct because it enables running interactive T-SQL queries directly against Parquet files in Azure Data Lake Storage Gen2 without provisioning any infrastructure. It uses a pay-per-query model and leverages a distributed query engine to deliver fast performance on petabytes of data, making it ideal for ad-hoc exploration.

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A. Azure SQL Database

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C. Azure HDInsight

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D. Azure Data Factory

Question 13

A data engineer needs to process raw clickstream data from multiple websites that is stored in Azure Blob Storage as JSON files. The processing must run automatically every hour, transform the data into a structured format for reporting, and handle schema changes in the source data without manual intervention. Which Azure service should be used?

Accepted Answer

Azure Data Factory with a Mapping Data Flow.. Azure Data Factory with a Mapping Data Flow is correct because it provides a code-free, visual data transformation environment that can run on a scheduled trigger (every hour), handle schema drift automatically via schema drift options in Mapping Data Flows, and process JSON files from Azure Blob Storage into a structured format for reporting. This meets all requirements: scheduled execution, transformation, and schema evolution without manual intervention.

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Azure Stream Analytics with a reference data input.

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Azure SQL Database with a stored procedure.

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Azure Logic Apps with a JSON parser.

Question 14

A data engineer needs to process streaming data from IoT devices and store the results in Azure Data Lake Storage for long-term analytics. The data must be processed in near real-time to detect anomalies and trigger alerts. Which Azure service should the engineer use for stream processing?

Accepted Answer

Azure Stream Analytics. Azure Stream Analytics is a serverless, real-time stream processing engine designed to handle high-velocity data from sources like IoT devices. It can ingest data from Azure Event Hubs or IoT Hub, apply SQL-based queries to detect anomalies in near real-time, and output results directly to Azure Data Lake Storage for long-term analytics. This makes it the correct choice for the described near-real-time anomaly detection and alerting requirement.

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Azure Data Factory

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Azure Analysis Services

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Azure Data Lake Analytics

Question 15

A company uses Azure Synapse Analytics to run complex queries against large datasets stored in Parquet files in Azure Data Lake Storage Gen2. They notice that queries scanning entire partitions are slow due to high I/O overhead on the compute nodes. Investigation shows each daily partition contains thousands of small files (under 1 MB each). Which optimization should be implemented first to improve query performance?

Accepted Answer

Compact small files into larger ones before querying. The high I/O overhead is caused by the thousands of small files per partition. When Synapse compute nodes read many small files, the overhead of opening, reading metadata, and closing each file dominates, even though the total data volume is small. Compacting these small files into fewer, larger files (e.g., 128 MB or more) reduces the number of file operations, improves read throughput, and allows more efficient predicate pushdown and parallelism.

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Increase the number of compute nodes

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Use columnstore indexes on external tables

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Change the partition column to a different date granularity