Power BI DA-100 -Examination with Well
Detailed Question & Answers Perfectly A+
Grade (Newest Updated Versions
What are the divisions of the core components of analytics? ANSWER>>>>To analyze
data, core components of analytics are divided into the following categories:
-Descriptive
-Diagnostic
-Predictive
-Prescriptive
-Cognitive
What are descriptive analytics? ANSWER>>>>Descriptive analytics help answer
questions about what has happened based on historical data. Descriptive analytics techniques
summarize large datasets to describe outcomes to stakeholders.
What are examples of descriptive analytics? ANSWER>>>>An example of descriptive
analytics is generating reports to provide a view of an organization's sales and financial data.
,What are diagnostic analytics? ANSWER>>>>Diagnostic analytics help answer questions
about why events happened. Diagnostic analytics techniques supplement basic descriptive
analytics, and they use the findings from descriptive analytics to discover the cause of these
events.
What are the steps of diagnostic analytics? ANSWER>>>>Performance indicators are
further investigated to discover why these events improved or became worse. Generally, this
process occurs in three steps:
1. Identify anomalies in the data. These anomalies might be unexpected changes in a metric or
a particular market.
2. Collect data that's related to these anomalies.
3. Use statistical techniques to discover relationships and trends that explain these anomalies.
What are predictive analytics? ANSWER>>>>Predictive analytics help answer questions
about what will happen in the future. Predictive analytics techniques use historical data to
identify trends and determine if they're likely to recur.
What techniques does predictive analytics use? ANSWER>>>>Techniques include a
variety of statistical and machine learning techniques such as neural networks, decision trees,
and regression.
What are prescriptive analytics? ANSWER>>>>Prescriptive analytics help answer
questions about which actions should be taken to achieve a goal or target. By using insights
from predictive analytics, organizations can make data-driven decisions. This technique allows
businesses to make informed decisions in the face of uncertainty.
What tools does prescriptive analytics rely on? ANSWER>>>>Prescriptive analytics
techniques rely on machine learning strategies to find patterns in large datasets.
,What are cognitive analytics? ANSWER>>>>Cognitive analytics 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. Cognitive analytics help you learn what might happen if circumstances
change and determine how you might handle these situations.
How are inferences structured for cognitive analytics? ANSWER>>>>Inferences aren't
structured queries based on a rules database; rather, they're unstructured hypotheses that are
gathered from several sources and expressed with varying degrees of confidence. Effective
cognitive analytics depend on machine learning algorithms, and will use several natural
language processing concepts to make sense of previously untapped data sources, such as call
center conversation logs and product reviews.
What are the different data roles? ANSWER>>>>-Business analyst
-Data analyst
-Data engineer
-Data scientist
-Database administrator
What is the role of the business analyst? ANSWER>>>>While some similarities exist
between a data analyst and business analyst, the key differentiator between the two roles is
what they do with data. A business analyst is closer to the business and is a specialist in
interpreting the data that comes from the visualization.
What is the role of the data analyst? ANSWER>>>>A data analyst enables businesses to
maximize the value of their data assets through visualization and reporting tools such as
Microsoft Power BI. Data analysts are responsible for profiling, cleaning, and transforming data.
Their responsibilities also include designing and building scalable and effective data models, and
enabling and implementing the advanced analytics capabilities into reports for analysis.
, What are the responsibilities of the data analyst with respect to Power BI
ANSWER>>>>A data analyst is also responsible for the management of Power BI assets,
including reports, dashboards, workspaces, and the underlying datasets that are used in the
reports. They are tasked with implementing and configuring proper security procedures, in
conjunction with stakeholder requirements, to ensure the safekeeping of all Power BI assets and
their data.
How do data analyst interreact with the data engineer and database administrator?
ANSWER>>>>Data analysts work with data engineers to determine and locate appropriate
data sources that meet stakeholder requirements. Additionally, data analysts work with the data
engineer and database administrator to ensure that the analyst has proper access to the
needed data sources. The data analyst also works with the data engineer to identify new
processes or improve existing processes for collecting data for analysis.
What is the role of the data engineer? ANSWER>>>>Data engineers provision and set up
data platform technologies that are on-premises and in the cloud. They manage and secure the
flow of structured and unstructured data from multiple sources. The data platforms that they
use can include relational databases, nonrelational databases, data streams, and file stores.
Data engineers also ensure that data services securely and seamlessly integrate across data
services.
Who does the data engineer work with? ANSWER>>>>As a data analyst, you would work
closely with a data engineer in making sure that you can access the variety of structured and
unstructured data sources because they will support you in optimizing data models, which are
typically served from a modern data warehouse or data lake.
Who can become a data engineer? ANSWER>>>>Both database administrators and
business intelligence professionals can transition to a data engineer role; they need to learn the
tools and technology that are used to process large amounts of data.
Detailed Question & Answers Perfectly A+
Grade (Newest Updated Versions
What are the divisions of the core components of analytics? ANSWER>>>>To analyze
data, core components of analytics are divided into the following categories:
-Descriptive
-Diagnostic
-Predictive
-Prescriptive
-Cognitive
What are descriptive analytics? ANSWER>>>>Descriptive analytics help answer
questions about what has happened based on historical data. Descriptive analytics techniques
summarize large datasets to describe outcomes to stakeholders.
What are examples of descriptive analytics? ANSWER>>>>An example of descriptive
analytics is generating reports to provide a view of an organization's sales and financial data.
,What are diagnostic analytics? ANSWER>>>>Diagnostic analytics help answer questions
about why events happened. Diagnostic analytics techniques supplement basic descriptive
analytics, and they use the findings from descriptive analytics to discover the cause of these
events.
What are the steps of diagnostic analytics? ANSWER>>>>Performance indicators are
further investigated to discover why these events improved or became worse. Generally, this
process occurs in three steps:
1. Identify anomalies in the data. These anomalies might be unexpected changes in a metric or
a particular market.
2. Collect data that's related to these anomalies.
3. Use statistical techniques to discover relationships and trends that explain these anomalies.
What are predictive analytics? ANSWER>>>>Predictive analytics help answer questions
about what will happen in the future. Predictive analytics techniques use historical data to
identify trends and determine if they're likely to recur.
What techniques does predictive analytics use? ANSWER>>>>Techniques include a
variety of statistical and machine learning techniques such as neural networks, decision trees,
and regression.
What are prescriptive analytics? ANSWER>>>>Prescriptive analytics help answer
questions about which actions should be taken to achieve a goal or target. By using insights
from predictive analytics, organizations can make data-driven decisions. This technique allows
businesses to make informed decisions in the face of uncertainty.
What tools does prescriptive analytics rely on? ANSWER>>>>Prescriptive analytics
techniques rely on machine learning strategies to find patterns in large datasets.
,What are cognitive analytics? ANSWER>>>>Cognitive analytics 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. Cognitive analytics help you learn what might happen if circumstances
change and determine how you might handle these situations.
How are inferences structured for cognitive analytics? ANSWER>>>>Inferences aren't
structured queries based on a rules database; rather, they're unstructured hypotheses that are
gathered from several sources and expressed with varying degrees of confidence. Effective
cognitive analytics depend on machine learning algorithms, and will use several natural
language processing concepts to make sense of previously untapped data sources, such as call
center conversation logs and product reviews.
What are the different data roles? ANSWER>>>>-Business analyst
-Data analyst
-Data engineer
-Data scientist
-Database administrator
What is the role of the business analyst? ANSWER>>>>While some similarities exist
between a data analyst and business analyst, the key differentiator between the two roles is
what they do with data. A business analyst is closer to the business and is a specialist in
interpreting the data that comes from the visualization.
What is the role of the data analyst? ANSWER>>>>A data analyst enables businesses to
maximize the value of their data assets through visualization and reporting tools such as
Microsoft Power BI. Data analysts are responsible for profiling, cleaning, and transforming data.
Their responsibilities also include designing and building scalable and effective data models, and
enabling and implementing the advanced analytics capabilities into reports for analysis.
, What are the responsibilities of the data analyst with respect to Power BI
ANSWER>>>>A data analyst is also responsible for the management of Power BI assets,
including reports, dashboards, workspaces, and the underlying datasets that are used in the
reports. They are tasked with implementing and configuring proper security procedures, in
conjunction with stakeholder requirements, to ensure the safekeeping of all Power BI assets and
their data.
How do data analyst interreact with the data engineer and database administrator?
ANSWER>>>>Data analysts work with data engineers to determine and locate appropriate
data sources that meet stakeholder requirements. Additionally, data analysts work with the data
engineer and database administrator to ensure that the analyst has proper access to the
needed data sources. The data analyst also works with the data engineer to identify new
processes or improve existing processes for collecting data for analysis.
What is the role of the data engineer? ANSWER>>>>Data engineers provision and set up
data platform technologies that are on-premises and in the cloud. They manage and secure the
flow of structured and unstructured data from multiple sources. The data platforms that they
use can include relational databases, nonrelational databases, data streams, and file stores.
Data engineers also ensure that data services securely and seamlessly integrate across data
services.
Who does the data engineer work with? ANSWER>>>>As a data analyst, you would work
closely with a data engineer in making sure that you can access the variety of structured and
unstructured data sources because they will support you in optimizing data models, which are
typically served from a modern data warehouse or data lake.
Who can become a data engineer? ANSWER>>>>Both database administrators and
business intelligence professionals can transition to a data engineer role; they need to learn the
tools and technology that are used to process large amounts of data.
