A data science team receives data from multiple sources in CSV, JSON, and XML formats. They need to store this data in a data lake for exploratory analysis, but they also need to run efficient queries that filter on specific columns. They want to minimize storage costs while maintaining flexibility. Which strategy should they use?

Store all data in Azure Cosmos DB

Store raw data as-is and create Parquet copies for querying

Store all data in Azure SQL Database

Convert all data to Parquet before storing

What is the primary purpose of a data warehouse?

To support online transaction processing (OLTP) for day-to-day operations

To provide a centralized repository for historical data optimized for analysis and reporting

To serve as a temporary staging area for data before it is loaded into other systems

To store raw, unprocessed data in its original format for future use

What type of service is Azure SQL Database?

A managed relational database service

A NoSQL document database service

A retail chain wants to build a reporting solution that combines sales data from its transactional database with clickstream data stored in a data lake. They need to run complex analytical queries that join these datasets and produce daily dashboards. They also want to minimize the need to move data between systems. Which approach should they take?

Load all data into Azure SQL Database and run queries there

Use Azure Synapse Analytics to query both the transactional database and the data lake in place

Copy the data lake into Azure Cosmos DB for querying

Store all data in Azure Blob Storage and use Azure Table Storage for queries

Which feature is typical of a data warehouse?

Data is optimized for high-volume insert and update operations

Data is denormalized and structured for fast analytical queries

Data is stored in a schema-on-read format without transformation

Data is stored in its raw, unprocessed form

Microsoft Certified:Azure Data Fundamentals

Domain 1Objective 2

Identify Options for Data Storage DP-900 Practice Questions (Page 3)

Part of the Describe core data concepts domain, which accounts for 25–30% of the DP-900 exam. Microsoft does not publish an official question count, but from its 45-minute exam (~20–30 total, ~5–9 in this domain), expect 1–2 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

9concepts

25–30%of the exam

Questions 11–15

11expert · hard
A data science team receives data from multiple sources in CSV, JSON, and XML formats. They need to store this data in a data lake for exploratory analysis, but they also need to run efficient queries that filter on specific columns. They want to minimize storage costs while maintaining flexibility. Which strategy should they use?
Select an answer first
12foundation · easy
What is the primary purpose of a data warehouse?
Select an answer first
13foundation · easy
What type of service is Azure SQL Database?
Select an answer first
14expert · hard
A retail chain wants to build a reporting solution that combines sales data from its transactional database with clickstream data stored in a data lake. They need to run complex analytical queries that join these datasets and produce daily dashboards. They also want to minimize the need to move data between systems. Which approach should they take?
Select an answer first
15foundation · easy
Which feature is typical of a data warehouse?
Select an answer first

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Identify Options for Data Storage DP-900 Practice Questions (Page 3)

Questions 11–15

What is the primary purpose of a data warehouse?

What type of service is Azure SQL Database?

Which feature is typical of a data warehouse?

Keep studying — Describe core data concepts