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Microsoft Azure Data Fundamentals DP-900 Practice Test

982 questions with instant explanations, domain breakdown, and wrong-answer analysis. Built for the real exam.

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Domain score breakdown
Real exam: 60 min
Pass mark: 700%

Sample questions with explanations

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Q1Describe core data conceptseasy
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A company stores customer names, addresses, and order history. They need to perform complex queries that join customer and order data. Which type of data store is most appropriate for this scenario?

AKey-value store
Relational databaseCorrect
CDocument database
DGraph database

A relational database (e.g., Azure SQL Database) is most appropriate because the scenario requires joining customer and order data via complex queries. Relational databases enforce a fixed schema with tables, primary keys, and foreign keys, enabling efficient JOIN operations usin…Read full explanation

Q2Identify considerations for relational data on Azuremedium
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A company is migrating an on-premises SQL Server database to Azure. They want to ensure that database administrators (DBAs) can perform administrative tasks but cannot view sensitive customer data in query results. Which Azure SQL feature should they implement?

ADynamic Data Masking
Always EncryptedCorrect
CTransparent Data Encryption
DRow-Level Security

Always Encrypted ensures that sensitive data is encrypted at all times, including during query processing, and that the encryption keys are never revealed to the database engine. This allows DBAs to perform administrative tasks (e.g., backups, index maintenance) while being unabl…Read full explanation

Q3Describe core data conceptsmedium
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A retail company captures real-time sensor data from IoT devices to detect anomalies and predict equipment failures. The data must be processed immediately as it arrives. Which type of data processing workload best describes this scenario?

ABatch processing
Streaming processingCorrect
COnline transaction processing (OLTP)
DData warehousing

B is correct because streaming processing is designed for continuous, real-time data ingestion and immediate analysis, which matches the requirement to process sensor data as it arrives. Technologies like Azure Stream Analytics or Apache Kafka enable low-latency processing of IoT…Read full explanation

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