DP-900 Describe core data concepts Practice Test 13 — 15 Questions

Question 1

A company needs to store semi-structured data from IoT devices, including temperature readings and device status. The data will be queried by time range and device ID. Which Azure data service is most cost-effective for this use case?

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

Accepted Answer

Azure Table Storage. Azure Table Storage is a NoSQL key-value store that is optimized for storing large amounts of semi-structured data without requiring a fixed schema. It supports efficient queries by partition key (device ID) and row key (timestamp), making it ideal for time-series IoT data at a lower cost than other Azure data services.

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Azure Blob Storage

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Azure Cosmos DB

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Azure SQL Database

Question 2

You need to design a data storage solution for an e-commerce platform that requires ACID transactions for order processing and high availability across regions. Which Azure service meets these requirements?

Accepted Answer

Azure SQL Database with active geo-replication. Azure SQL Database with active geo-region replication supports ACID transactions natively and provides automatic failover to a secondary region, ensuring high availability across regions. This meets the e-commerce platform's need for transactional consistency and regional resilience.

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Azure Database for MySQL with read replicas

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Azure Synapse Analytics

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Azure Cosmos DB with multiple write regions

Question 3

Which TWO Azure services are primarily used for batch processing of large volumes of data? (Choose two.)

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics (A) is correct because it is a cloud-based analytics service that combines big data (Spark) and data warehousing (SQL pools) to process and analyze massive volumes of data in batch workloads. Azure Databricks (D) is correct because it is an Apache Spark-based analytics platform designed for large-scale batch data processing, machine learning, and ETL jobs. Azure SQL Database (B) is a transactional relational database service optimized for OLTP workloads, not large-scale batch processing. Azure Stream Analytics (C) is a real-time streaming analytics service for continuous event data, not batch processing. Azure Data Lake Storage (E) is a scalable storage layer for big data, but it stores data rather than performing batch processing itself.

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Azure SQL Database

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Azure Stream Analytics

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Azure Data Lake Storage

Question 4

Which THREE are characteristics of structured data? (Choose three.)

Accepted Answer

Has a predefined schema. Structured data has a predefined schema, meaning the data types, relationships, and constraints are defined before data is entered. This schema ensures consistency and enables efficient querying, which is why relational databases enforce a fixed schema through table definitions and constraints like primary keys and foreign keys.

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Consists of audio and video files

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Uses JSON or XML format

Question 5

Which TWO are benefits of using a NoSQL database like Azure Cosmos DB? (Choose two.)

Accepted Answer

Horizontal scalability. Option C (Horizontal scalability) is correct because Azure Cosmos DB is designed to scale out by partitioning data across many physical nodes, allowing throughput and storage to grow elastically without vertical hardware upgrades. Option D (Schema flexibility) is correct because NoSQL databases like Cosmos DB store JSON documents whose properties can vary per item, so the data model can evolve without migrations or a fixed table schema. Option A is incorrect because enforcing referential integrity is a hallmark of relational databases with foreign keys, which Cosmos DB does not enforce across containers. Option B is incorrect because complex multi-entity joins are a relational strength; Cosmos DB favors denormalization and embedded documents rather than SQL-style joins. Option E is incorrect as a general NoSQL benefit because full multi-document ACID transactions are not universal to NoSQL, and Cosmos DB only offers them within a single logical partition, so it is not a defining benefit of the category.

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Enforcing referential integrity

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Support for complex joins

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Full ACID transactions across multiple documents

Question 6

Refer to the exhibit. The JSON shows an Azure Policy definition. Which effect should be used to proactively prevent creation of storage accounts without encryption?

Accepted Answer

Deny. The 'Deny' effect is correct because it proactively blocks the creation or update of a storage account that does not meet the encryption requirement, preventing non-compliant resources from being provisioned. This aligns with Azure Policy's ability to enforce compliance at resource creation time, rather than auditing or remediating after the fact.

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AuditIfNotExists

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Disabled

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Append

Question 7

Refer to the exhibit. A data engineer needs to query the orders.csv file using Azure Synapse Serverless SQL. What is the most efficient way to access this data?

Accepted Answer

Use OPENROWSET in Serverless SQL. Azure Synapse Serverless SQL is designed for on-demand querying of data stored in data lakes without provisioning storage. The OPENROWSET function with the BULK option allows direct querying of CSV files using T-SQL, making it the most efficient method for ad-hoc analysis of the orders.csv file without data movement or schema management.

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Use PolyBase to create external table

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Copy data to Azure SQL Database using ADF

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Load data into a dedicated SQL pool

Question 8

Refer to the exhibit. The JSON shows a configuration for which Azure service?

Accepted Answer

Azure Data Factory. The JSON snippet defines a pipeline with a copy activity that moves data from a source (Azure Blob Storage) to a sink (Azure SQL Database). This is the core pattern of Azure Data Factory (ADF), which orchestrates and automates data movement and transformation. The structure with 'name', 'properties', 'activities', 'typeProperties', 'source', and 'sink' is specific to ADF pipeline definitions.

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Azure Analysis Services

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Power BI

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Azure Synapse Analytics

Question 9

Your organization uses Azure SQL Database and needs to ensure that all customer data is encrypted at rest and in transit with minimal administrative overhead. Which solution should you recommend?

Accepted Answer

Enable Transparent Data Encryption (TDE) and enforce TLS 1.2 for connections.. Transparent Data Encryption (TDE) encrypts Azure SQL Database data files at rest without requiring any application changes, and enforcing TLS 1.2 ensures all data in transit is encrypted using a strong, industry-standard protocol. This combination meets the requirement for encryption at rest and in transit with minimal administrative overhead, as TDE is managed by the platform and TLS enforcement is a simple server-level setting.

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Use Microsoft Purview Information Protection to label and encrypt the data.

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Implement Dynamic Data Masking on the customer table.

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Enable Always Encrypted for all sensitive columns and use client-side encryption.

Question 10

A data analyst needs to query a large dataset stored in Azure Blob Storage using serverless SQL pool in Azure Synapse Analytics. Which data format should they use to minimize storage costs while still supporting efficient querying?

Accepted Answer

Parquet. Parquet is a columnar storage format that compresses data efficiently and supports predicate pushdown, allowing serverless SQL pool in Azure Synapse to read only the necessary columns and rows. This minimizes storage costs while maintaining high query performance, unlike row-oriented formats such as CSV or JSON.

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CSV

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JSON

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Avro

Question 11

Your company is designing a data solution for IoT sensor data that arrives in high volume and must be stored for long-term analytics. The data is append-only and rarely updated. You need to choose a storage solution that balances cost and query performance for historical analysis. Which Azure data store should you recommend?

Accepted Answer

Azure Data Lake Storage Gen2. Azure Data Lake Storage Gen2 is the correct choice because it combines a hierarchical namespace with Azure Blob Storage, offering scalable, cost-effective storage for high-volume append-only data like IoT sensor logs. It supports both structured and unstructured data, integrates with analytics engines like Azure Synapse and Spark, and provides POSIX-compliant access control, making it ideal for long-term historical analysis at low cost.

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Azure Cosmos DB

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Azure Table Storage

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Azure SQL Database

Question 12

You are designing a data pipeline that ingests sales transactions from an on-premises SQL Server database into Azure Synapse Analytics for reporting. The data must be processed incrementally every hour with minimal latency. Which Azure service should you use to orchestrate the pipeline?

Accepted Answer

Azure Data Factory. Azure Data Factory (ADF) is the correct choice because it is a cloud-based ETL and data orchestration service designed specifically for building complex, schedule-driven pipelines. It natively supports incremental data loading from on-premises SQL Server via self-hosted integration runtime, and can trigger pipelines on an hourly schedule with minimal latency, making it ideal for this scenario.

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Azure Logic Apps

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Azure Databricks

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Azure Functions

Question 13

Your team is migrating a data warehouse to Azure Synapse Analytics. You need to ensure that the data model supports both historical trend analysis and current-day reporting with minimal storage redundancy. Which table design pattern should you use?

Accepted Answer

Star schema with dimension and fact tables. The star schema is the correct choice because it separates business processes into fact tables (for measures like sales quantities) and dimension tables (for descriptive attributes like customer or date). This design directly supports both historical trend analysis (by joining facts with the date dimension) and current-day reporting (by filtering on the latest date) while minimizing storage redundancy through normalized dimensions. Azure Synapse Analytics is optimized for star schemas, leveraging columnstore indexes and distributed tables to accelerate such queries.

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Single flat table containing all attributes

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Wide table with repeated customer attributes per order

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Highly normalized design with many tables

Question 14

A healthcare organization stores patient records in Azure Blob Storage and must comply with data retention policies that require deleting records after 7 years. They also need to prevent any modification or deletion of records before the retention period ends. Which Azure feature should they use?

Accepted Answer

Immutable storage with time-based retention policy. Immutable storage with a time-based retention policy (WORM – Write Once, Read Many) ensures that blobs cannot be modified or deleted until the retention period expires. This directly meets the dual requirement of preventing premature deletion while enforcing a 7-year retention, as the policy locks the data for the specified duration.

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Azure Backup for Blob Storage

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Soft delete for Blob Storage

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Azure Blob Storage lifecycle management

Question 15

You need to design a real-time dashboard that displays the number of orders placed in the last hour from an e-commerce application. The application writes orders to Azure Event Hubs. Which Azure service should you use to aggregate the data and serve the dashboard with minimal latency?

Accepted Answer

Azure Stream Analytics with Power BI output. Azure Stream Analytics is purpose-built for real-time data processing from sources like Event Hubs, and its native integration with Power BI enables direct output to a dashboard with sub-second latency. This combination provides the minimal-latency aggregation and serving required for a real-time orders dashboard without additional infrastructure.

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Azure Databricks Structured Streaming

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Azure Analysis Services

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Azure Data Factory with tumbling window