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Prepare data for analysis Microsoft Quiz Answers
Get Prepare data for analysis Microsoft Quiz Answers
You will explore Power Query as you learn to extract data from different data sources and choose a storage mode and connectivity type. You will learn to profile, clean, and load data into Power BI in preparation for modeling your data.
This learning path can help you prepare for the Microsoft Certified: Data Analyst Associate certification.
This learning path helps prepare you for Exam PL-300: Microsoft Power BI Data Analyst.
Prerequisites:
- None
Module 1: Get data in Power BI
You will learn how to retrieve data from a wide variety of data sources, including Microsoft Excel, relational databases, and NoSQL data stores. You will also learn how to improve performance while retrieving data.
Learning objectives:
By the end of this module, you’ll be able to:
- Identify and connect to a data source
- Get data from a relational database, like Microsoft SQL Server
- Get data from a file, like Microsoft Excel
- Get data from applications
- Get data from Azure Analysis Services
- Select a storage mode
- Fix performance issues
- Resolve data import errors
Prerequisites:
- None
Quiz 1: Check your knowledge
Q1. What type of expression do you use to extract data from Microsoft SQL Server?
- DAX
- T-SQL
- MDX
Q2. You’re creating a Power BI report with data from an Azure Analysis Services Cube. When the data refreshes in the cube, you would like to see it immediately in the Power BI report. How should you connect?
- Import
- T-SQL
- Live connection
Q3. What can you do to improve performance when you’re getting data in Power BI?
- Use query folding
- Only pull data into the Power BI service, not Power BI Desktop
- Use the Select SQL statement in your SQL queries when you’re pulling data from a relational database.
- Combine date and time columns into a single column
Module 2: Clean, transform, and load data in Power BI
Power Query has an incredible amount of features that are dedicated to helping you clean and prepare your data for analysis. You will learn how to simplify a complicated model, change data types, rename objects, and pivot data. You will also learn how to profile columns so that you know which columns have the valuable data that you’re seeking for deeper analytics.
Learning objectives:
By the end of this module, you’ll be able to:
- Resolve inconsistencies, unexpected or null values, and data quality issues.
- Apply user-friendly value replacements.
- Profile data so you can learn more about a specific column before using it.
- Evaluate and transform column data types.
- Apply data shape transformations to table structures.
- Combine queries.
- Apply user-friendly naming conventions to columns and queries.
- Edit M code in the Advanced Editor.
Prerequisites:
- None
Quiz 1: Check your knowledge
Q1. What is a risk of having null values in a numeric column?
- That function SUM of data will be incorrect.
- That function MAX of data will be incorrect.
- That function AVERAGE of data will be incorrect.
Q2. If you have two queries that have different data but the same column headers, and you want to combine both tables into one query with all the combined rows, which operation should you perform?
- Append
- Merge
- Combine Column
Q3. Which of the following selections are not best practices for naming conventions in Power BI?
- Rename columns to have spaces in them.
- Abbreviate column names.
- Replace values that have integers with human readable results.
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This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.
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