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Microsoft DA-100 > Data Analysis > Flashcards

Data Analysis Flashcards

1
Q

Help answer questions about what has happened based on historical data

A

Descriptive Analytics

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2
Q

Summarize large datasets to describe outcomes to stakeholders

A

Descriptive Analytics

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3
Q

By developing key performance indicators (KPIs), these strategies can help track the success or failure of key objectives.

A

Descriptive Analytics

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4
Q

Generate reports to provide a view of an organization’s sales and financial data.

A

Descriptive Analytics

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5
Q

Identify anomalies in the data.

A

Diagnostic Analytics

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6
Q

Answer questions about why events happened.

A

Diagnostic Analytics

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7
Q

Supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events.

A

Diagnostic Analytics

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8
Q

Collect data that’s related to these anomalies.

A

Diagnostic Analytics

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9
Q

Use statistical techniques to discover relationships and trends that explain these anomalies.

A

Diagnostic Analytics

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10
Q

Help answer questions about what will happen in the future.

A

Predictive Analytics

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11
Q

Use historical data to identify trends and determine if they’re likely to recur.

A

Predictive Analytics

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12
Q

Help answer questions about which actions should be taken to achieve a goal or target.

A

Prescriptive Analytics

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13
Q

Use insights from predictive analytics, organizations can make data-driven decisions.

A

Prescriptive Analytics

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14
Q

Rely on machine learning strategies to find patterns in large datasets.

A

Prescriptive Analytics

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15
Q

Attempt to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings back into the knowledge base for future inferences, a self-learning feedback loop.

A

Cognitive Analytics

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16
Q

Help you learn what might happen if circumstances change and determine how you might handle these situations.

A

Cognitive Analytics

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17
Q

Closer to the business and is a specialist in interpreting the data that comes from the visualization.

A

Business Analyst

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18
Q

Enable businesses to maximize the value of their data assets through visualization and reporting tools such as Microsoft Power BI.

A

Data Analyst

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19
Q

Responsible for profiling, cleaning, and transforming data.

A

Data Analyst

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20
Q

Design and build scalable and effective data models, and enabling and implementing the advanced analytics capabilities into reports for analysis.

A

Data Analyst

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21
Q

Turn raw data into relevant and meaningful insights.

A

Data Analyst

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22
Q

Responsible for the management of Power BI assets, including reports, dashboards, workspaces, and the underlying datasets that are used in the reports.

A

Data Analyst

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23
Q

Implement and configure proper security procedures, in conjunction with stakeholder requirements, to ensure the safekeeping of all Power BI assets and their data.

A

Data Analyst

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24
Q

Work with data engineers to determine and locate appropriate data sources that meet stakeholder requirements.

A

Data Analyst

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25
Q

Works with the data engineer to identify new processes or improve existing processes for collecting data for analysis.

A

Data Analyst

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26
Q

Provision and set up data platform technologies that are on-premises and in the cloud.

A

Data Engineer

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27
Q

Manage and secure the flow of structured and unstructured data from multiple sources.

A

Data Engineer

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28
Q

Ensure that data services securely and seamlessly integrate across data services.

A

Data Engineer

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29
Q

Use of on-premises and cloud data services and tools to ingest, egress, and transform data from multiple sources.

A

Data Engineer

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30
Q

Collaborate with business stakeholders to identify and meet data requirements. They design and implement solutions.

A

Data Engineer

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31
Q

Perform advanced analytics to extract value from data.

A

Data Scientist

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32
Q

Might work in the realm of deep learning, performing iterative experiments to solve a complex data problem by using customized algorithms.

A

Data Scientist

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33
Q

Implements and manages the operational aspects of cloud-native and hybrid data platform solutions that are built on Microsoft Azure data services and Microsoft SQL Server.

A

Database administrator

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34
Q

Responsible for the overall availability and consistent performance and optimizations of the database solutions.

A

Database administrator

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35
Q

Monitors and manages the overall health of a database and the hardware that it resides on

A

Database administrator

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36
Q

Responsible for managing the overall security of the data, granting and restricting user access and privileges to the data as determined by business needs and requirements.

A

Database administrator

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37
Q

Process of profiling, cleaning, and transforming your data to get it ready to model and visualize.

A

Data Preparation

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38
Q

The process of taking raw data and turning it into information that is trusted and understandable. It involves, among other things, ensuring the integrity of the data, correcting wrong or inaccurate data, identifying missing data, converting data from one structure to another or from one type to another, or even a task as simple as making data more readable.

A

Data Preparation

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39
Q

Understanding how you’re going to get and connect to the data and the performance implications of the decisions.

A

Data Preparation

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40
Q

Process of determining how your tables are related to each other. This process is done by defining and creating relationships between the tables. From that point, you can enhance the model by defining metrics and adding custom calculations to enrich your data.

A

Data Modeling

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41
Q

Prepare, Model, Visualize, Analyze and Manage

A

Data Analyst Tasks

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42
Q

Has a direct effect on the performance of your report and overall data analysis.

A

Data Modeling

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43
Q

Bring your data to life

A

Data Visualization

44
Q

Goal is to solve business problems.

A

Data Visualization

45
Q

Reports should be designed with accessibility in mind from the outset so that no special modifications are needed in the future.

A

Data Visualization

46
Q

Understanding and interpreting the information that is displayed on the report.

A

Data Analyzation

47
Q

Organizations can drill into the data to predict future patterns and trends, identify activities and behaviors, and enable businesses to ask the appropriate questions about their data.

A

Advanced Analytics

48
Q

AI integrations within Power BI can take your analysis to the next level. Integrations with Azure machine learning, cognitive services, and built-in AI visuals will help to enrich your data and analysis.

A

Data Analyzation

49
Q

Help reduce data silos within your organization.

A

Manage Data

50
Q

Reduce data silos with the use of shared datasets, and it allows you to reuse data that you have prepared and modeled.

A

Manage Data

51
Q

Microsoft Windows desktop application called Power BI Desktop

52
Q

Online SaaS (Software as a Service) service called the Power BI service

53
Q

Mobile Power BI apps that are available on any device, with native mobile BI apps for Windows, iOS, and Android.

54
Q

Visualizations, Datasets, Reports, Dashboards, and Tiles

A

Power BI Building Blocks

55
Q

Visual representation of data, like a chart, a color-coded map, or other interesting things you can create to represent your data visually.

A

Visualizations

56
Q

Collection of data that Power BI uses to create its visualizations.

57
Q

Combination of many different sources, which you can filter and combine to provide a unique collection of data for use in Power BI.

58
Q

Collection of visualizations that appear together on one or more pages.

59
Q

Let you create many visualizations, on multiple pages if necessary, and let you arrange those visualizations in whatever way best tells your story.

60
Q

Single visualization on a report or a dashboard.

61
Q

Collection of preset, ready-made visuals and reports that are shared with an entire organization.

62
Q

Shows you the available sources of data in the Power BI service.

63
Q

Identify each unique, non-null data row.

A

Primary Keys

64
Q

Reference rows in a different table

A

Foreign Keys

65
Q

Contain observational or event data values: sales orders, product counts, prices, transactional dates and times, and quantities.

A

Fact Tables

66
Q

Contain the details about the data in fact tables: products, locations, employees, and order types.

A

Dimension Tables

67
Q

Edit the name and description of the column.

A

Model - General Tab

68
Q

Add synonyms that can be used to identify the column when you are using the Q&A feature.

A

Model - General Tab

69
Q

Add a column into a folder to further organize the table structure.

A

Model - General Tab

70
Q

Hide or show the column.

A

Model - General Tab

71
Q

Change the data type.

A

Model - Formatting Tab

72
Q

Format the date.

A

Model - Formatting Tab

73
Q

Sort by a specific column.

A

Model - Advanced Tab

74
Q

Assign a specific category to the data.

A

Model - Advanced Tab

75
Q

Summarize the data.

A

Model - Advanced Tab

76
Q

Determine if the column or table contains null values.

A

Model - Advanced Tab

77
Q

Source data tables are mature and ready for immediate use. Identify company holidays. Separate calendar and fiscal year. Identify weekends versus weekdays.

A

Source Data Date Table

78
Q

Dates = CALENDAR(DATE(2011, 5, 31), DATE(2021, 5, 31))

A

DAX Date Table

79
Q

The CALENDAR() function returns a contiguous range of dates based on a start and end date that are entered as arguments in the function.

A

DAX Date Table

80
Q

The CALENDARAUTO() function returns a contiguous, complete range of dates that are automatically determined from your dataset.

A

DAX Date Table

81
Q

MonthNum = MONTH(Dates[Date])

A

DAX Date Table

82
Q

WeekNum = WEEKNUM(Dates[Date])

A

DAX Date Table

83
Q

DayoftheWeek = FORMAT(Dates[Date].[Day], “DDDD”)

A

DAX Date Table

84
Q

Use M-language, the development language that is used to build queries in Power Query.

A

Power Query Date Table

85
Q

Form through natural segments in your data.

A

Hierarchies

86
Q

The process of viewing multiple child levels based on a top-level parent.

A

Flatten the hierarchy

87
Q

Create multiple columns in a table to show the hierarchical path of the parent to the child in the same record.

A

Flatten the hierarchy

88
Q

Path() returns a text version of the hierarchical path which can be split into multiple columns and turned into a hierarchy

A

Flatten the hierarchy

89
Q

Have multiple valid relationships with fact tables, meaning that the same dimension can be used to filter multiple columns or tables of data. As a result, you can filter data differently depending on what information you need to retrieve.

A

Role-playing dimensions

90
Q

Requires complex DAX functions.

A

Role-playing dimensions

91
Q

Detail represented within your data

A

Data Granularity

92
Q

Describes a relationship in which you have many instances of a value in one column that are related to only one unique corresponding instance in another column.

A

Many-to-one or one-to-many cardinality

93
Q

Describes the directionality between fact and dimension tables.

A

Many-to-one or one-to-many cardinality

94
Q

Is the most common type of directionality and is the Power BI default when you are automatically creating relationships.

A

Many-to-one or one-to-many cardinality

95
Q

Describes a relationship in which only one instance of a value is common between two tables.

A

One-to-one cardinality

96
Q

Requires unique values in both tables.

A

One-to-one cardinality

97
Q

Is not recommended because this relationship stores redundant information and suggests that the model is not designed correctly. It is better practice to combine the tables.

A

One-to-one cardinality

98
Q

Describes a relationship where many values are in common between two tables.

A

Many-to-many cardinality

99
Q

Does not require unique values in either table in a relationship.

A

Many-to-many cardinality

100
Q

Is not recommended; a lack of unique values introduces ambiguity and your users might not know which column of values is referring to what.

A

Many-to-many cardinality

101
Q

Only one table in a relationship can be used to filter the data. For instance, Table 1 can be filtered by Table 2, but Table 2 cannot be filtered by Table 1.

A

Single cross-filter direction

102
Q

For a one-to-many or many-to-one relationship, the cross-filter direction will be from the “one” side, meaning that the filtering will occur in the table that has unique values.

A

Single cross-filter direction

103
Q

One table in a relationship can be used to filter the other. For instance, a dimension table can be filtered through the fact table, and the fact tables can be filtered through the dimension table.

A

Bi-direction Cross Filter

104
Q

You might have lower performance when using bi-directional cross-filtering with many-to-many relationships.

A

Bi-direction Cross Filter

105
Q

One-to-one relationships filter option.

A

Bi-direction Cross Filter

106
Q

Many-to-many relationships filter options

A

Bi-direction Cross Filter; Single cross-filter direction

Microsoft DA-100 (1 decks)

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