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Understand data concepts Microsoft Quiz Answers
Get Understand data concepts Microsoft Quiz Answers
Data is key to most business operations and services and an understanding of data is essential in many roles. This learning path covers data concepts, data analytics, and data roles, services, and products
Prerequisites:
- You should be familiar with basic computing concepts and terminology.
Module 1: Get started with data concepts
Businesses rely on data. Understanding data is key to many business operations. This module covers what data is, when to sort and filter data, and when to calculate derived values from data.
Learning objectives:
By the end of this module, you will be able to:
- Describe data
- Describe the need to sort and filter data
- Describe the need to calculate derived values
- Describe the need to highlight data
Prerequisites:
- You should be familiar with basic computing concepts and terminology.
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. Which data type would you use to store product prices?
- A numeric type
- A date type
- A string type
Q2. What happens when filters are applied to a dataset?
- Non-matching rows are excluded from the result set
- Rows that don’t match the filters are deleted from the data source
- The data is sorted in ascending order
Q3. What is derived data?
- A table of columns and rows copied from another dataset
- A new row added to the dataset
- A new column added to a dataset using data that exists in the dataset
Q4. What is the key purpose of visual indicators?
- They make reports more aesthetic
- They cause other processes to run
- They enable you to quickly see patterns in the data
Module 2: Get started with data analytics
Converting raw data to useful information is essential for business users. This module covers fundamental concepts of aggregating data and grouping data.
Learning objectives:
By the end of this module, you will be able to:
- Describe data aggregation
- Describe grouping of data
- Describe data visualizations
Prerequisites:
- You should be familiar with basic computing concepts and terminology.
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. Which of the following aggregate functions can’t be applied to a column with a string data type?
- Count
- Count distinct
- Sum
Q2. To uncover the revenue of the New York store in your dataset, what approach would you use?
- Group the dataset by the Store field, and summarize the Revenue field
- Count the number of rows where the Store value is equal to New York
- Delete all rows where the store isn’t equal to New York and then summarize the Revenue field
Q3. Which of the following columns in a dataset would be an appropriate use of a color scale indicator?
- Temperature
- Day
- LastName
Module 3: Understand data roles, services, and products
Data is so widespread that many services and roles are needed to manage the data of an organization. This module covers types of data products, Azure data services, and the roles of data professionals.
Learning objectives:
By the end of this module, you will be able to:
- Describe categories of data products
- Describe Azure data services
- Describe data professional roles
Prerequisites:
- You should be familiar with basic computing concepts and terminology.
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. When should you use a spreadsheet for analyzing data?
- When you work alone or in a team, and need to collaborate on a project with a small dataset
- When you want to collate invoices and purchase orders together
- When you want to process data feeds from third-party applications
Q2. Why would you use Microsoft Azure Synapse Analytics?
- To develop a database for relational and transactional systems
- To build a data warehouse using data from multiple systems
- To develop a database for an application that stores nonrelational data
Q3. Which of the following data professionals is responsible for modeling data and creating reports and dashboards?
- Data scientist
- Database administrator
- Data analyst
Conclusion:
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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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