Microsoft Analyzing Data with Microsoft Power BI (DA-100日本語版) - DA-100日本語

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4. Data Analysis (10-15%):

This section requires your skills in:

• Caring out advanced analysis: This subject area covers your skills in identifying outliers, using binnings & groupings, conducting Time Series Analysis, and applying AI Insights. The students should also have competence in using decomposition tree visuals to dissect a measure and use Key Influencers for the exploration of dimensional variances.
• Enhancing reports to expose insights: The candidates should have the expertise in personalizing visuals, applying filters & slicers and conditional formatting. They also need to demonstrate their skills in exploring statistical summary, creating reference lines with Analytics pane, and using Q&A visuals, among others.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

What should you know about the Microsoft DA-100 exam before going for it?

The intended candidates for the Microsoft DA-100 exam are Data Analysts. These professionals help the organizations make the most of the value of data assets through the use of Microsoft Power BI. They also collaborate with the major stakeholders across the company to deliver the appropriate insights based on the recognized business prerequisites.

Microsoft DA-100 is an associate-level certification test, and as such, it does not have any official prerequisites. However, Microsoft recommends that the students have a good fundamental knowledge of data processing and data repositories both in Cloud and on-premises. The candidates for the exam should have the relevant skills in building and designing scalable data models, facilitating advanced analytic abilities, and transforming and cleaning data to provide significant business value through understandable data visualizations.

Understanding functional and technical aspects of Exam DA-100: Analyzing Data with Microsoft Power BI Prepare the data

The following will be discussed in MICROSOFT DA-100 exam dumps:

• Interrogate data statistics
• Identify query performance issues
• Identify and create appropriate keys for joins
• Evaluate and transform column data types
• Select a storage mode
• Resolve inconsistencies, unexpected or null values, and data quality issues
• Select a shared dataset or create a local dataset
• Use or create a data flow
• Apply user-friendly value replacements
• Apply data shape transformations to table structures
• Change data source settings
• Choose an appropriate query type
• Interrogate column properties
• Use parameters
• Identify data anomalies
• Identify and connect to a data source
• Combine queries
• Leverage Advanced Editor to modify Power Query M code
• Resolve data import errors
• Apply user-friendly naming conventions to columns and queries
• Use or create a PBIDS file
• Use Microsoft Dataverse
• Configure data loading
• Examine data structures

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