Microsoft Certified Data Analyst Associate Study Guide 2026
Everything you need to pass the Microsoft Certified Data Analyst Associate exam in one place: the exam format, every topic to study, real practice questions with explanations, flashcards, and full-length practice tests. Free, no sign-up needed.
📋 Microsoft Certified Data Analyst Associate Exam Format at a Glance
📚 Microsoft Certified Data Analyst Associate Topics to Study (21)
✍️ Sample Microsoft Certified Data Analyst Associate Questions & Answers
1. Which relationship cardinality type is most commonly used between fact tables and dimension tables in a star schema?
One-to-many relationships are the standard in star schema designs, where one dimension record relates to many fact records.
2. What is the difference between a measure and a calculated column in Power BI?
Measures evaluate dynamically based on filter context at query time, while calculated columns are computed during refresh and stored in the model.
3. A business hosts gatherings. Customers must be able to see forthcoming conferences that your business will be hosting. Which component of the Power Apps should you use?
A Power Apps portal is the ideal component for hosting public-facing websites that allow external users, like customers, to view information and interact with business data. Unlike canvas or model-driven apps, which are typically for internal users, portals are designed for broad public access and can display forthcoming events or conferences.
4. What does the 'Merge Queries' operation in Power Query do?
Merge Queries combines two tables based on matching key columns using join types such as Left Outer, Inner, or Full Outer.
5. What does BLANK() represent in DAX?
BLANK() represents the absence of a value (similar to NULL), and most DAX functions treat blank as zero in arithmetic but as missing in text operations.
6. Which sort of data workload stores a lot of aggregated data and is best for read operations?
Analytical data workloads are specifically designed for processing and querying large volumes of historical and aggregated data. They are optimized for read-heavy operations, enabling users to perform complex analyses, generate reports, and derive insights from the data. This contrasts with transactional workloads, which prioritize rapid, individual write operations.