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Power BI : DAX Formula and Storage Engines and Query Evaluation

Quiz by Support - BusinessPromoted .com

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10 questions
Show answers
  • Q1
    What are the two main engines used in Power BI for processing queries?
    Calculation Engine and Data Engine
    Visualization Engine and Data Engine
    Formula Engine and Storage Engine
    Query Engine and Data Engine
    30s
  • Q2
    How does the Formula Engine handle DAX queries?
    It fetches data from the data source
    It stores the data in memory
    It processes logical and mathematical operations
    It renders visualizations
    30s
  • Q3
    What is the primary role of the Storage Engine in Power BI?
    To retrieve data from the data source
    To calculate DAX expressions
    To render visualizations
    To create data models
    30s
  • Q4
    Which engine in Power BI is responsible for scanning data and retrieving results?
    Data Engine
    Formula Engine
    Storage Engine
    Calculation Engine
    30s
  • Q5
    What kind of operations are handled by the Formula Engine?
    Data visualization
    Logical and mathematical operations
    Data storage
    Data retrieval
    30s
  • Q6
    What does VertiPaq refer to in the context of Power BI?
    The data modeling interface
    The visualization tool
    The in-memory columnar storage engine
    The DAX calculation language
    30s
  • Q7
    How does the Storage Engine improve query performance?
    By reducing data granularity
    By re-evaluating DAX expressions
    By using in-memory storage
    By rendering visualizations faster
    30s
  • Q8
    Which type of data storage is used by VertiPaq to enhance performance?
    Columnar storage
    File-based storage
    Hybrid storage
    Row-based storage
    30s
  • Q9
    What is a key benefit of using DAX variables in queries?
    Simplified data visualization
    Enhanced data security
    Faster data storage
    Improved code readability and reusability
    30s
  • Q10
    How do DAX queries interact with the Storage Engine?
    By retrieving pre-aggregated data
    By rendering data visualizations
    By modifying the data model
    By storing data externally
    30s

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