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DA-100 Power BI With 100% ACCURATE QUESTIONS AND ANSWERS GRADED A+
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DA-100 Power BI With 100% ACCURATE QUESTIONS AND ANSWERS GRADED A+
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JACKLINEDA-100 Power BI With 100% ACCURATE QUESTIONS AND ANSWERS GRADED A+
7
Terms in this set (402)
Prepare the Data (20-25%)
Model the Data (20-30%)
Skills Measured Visualize the Data (20-25%)
Analyze the Data (10-15%)
Deploy and Maintain Deliverables (10-15%)
Descriptive
Diagnostic
Core components of analytics Predictive
Prescriptive
Cognitive
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Help answer questions about what has happened based on historical data. Descriptive
analytics techniques summarize large datasets to describe outcomes to stakeholders.
By developing key performance indicators (KPIs), these strategies can help track the success
Descriptive Analytics or failure of key objectives. Metrics such as return on investment (ROI) are used in many
industries, and specialized metrics are developed to track performance in specific industries.
An example of descriptive analytics is generating reports to provide a view of an organization's
sales and financial data.
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. Then, performance indicators are further investigated to
discover why these events improved or became worse.
Diagnostic Analytics
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.
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. Predictive analytical
tools provide valuable insight into what might happen in the future.
Predictive Analytics
Techniques include a variety of statistical and machine learning techniques such as neural
networks, decision trees, and regression.
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.
Cognitive Analytics
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.
While some similarities exist between a data analyst and business analyst, the key differentiator
Business Analyst 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. Often, the roles of
DA-100 Power BI data analyst and business analyst could be the responsibility of a single person.
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(Prepare, Model, Visualize, Analyze, Manage)
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. A data analyst works with the pertinent stakeholders to
identify appropriate and necessary data and reporting requirements, and then they are tasked
with turning raw data into relevant and meaningful insights.
Data Analyst
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.
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.
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.
Data engineers collaborate with business stakeholders to identify and meet data requirements.
Data Engineer They design and implement solutions.
While some alignment might exist in the tasks and responsibilities of a data engineer and a
database administrator, a data engineer's scope of work goes well beyond looking after a
database and the server where it's hosted and likely doesn't include the overall operational
data management.
A data engineer adds tremendous value to business intelligence and data science projects.
When the data engineer brings data together, often described as data wrangling, projects
move faster because data scientists can focus on their own areas of work.
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