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Difficulty in Attempting Exam DA-100: Analyzing Data with Microsoft Power BI

However, if you would like to use different source data/tables in the same project, link them together and perform transformations on the data in Power BI as well, then the level of difficulty increases. Learning this will take of course more time. What we believe will also help is to get a general understanding of how data cleansing works and data integration in general. We offer MICROSOFT DA-100 practice exams and MICROSOFT DA-100 practice test for this purpose.

Analyzing Data with Microsoft Power BI Certification helps candidates in developing their professionals and academic career and it is a very tough task to pass Microsoft DA-100 exam for those candidates who have not done hard work and get some relevant Microsoft DA-100 exam preparation material. There are many peoples have passed Microsoft DA-100 exam by following these three things such as look for the latest Microsoft DA-100 exam dumps, get relevant MICROSOFT DA-100 exam dumps and develop their knowledge about Microsoft DA-100 exam new questions. At the same time, it can also stress out some people as they found passing Microsoft DA-100 exam a tough task. It is just a wrong assumption as many of the peoples have passed Microsoft DA-100 exam questions. All you have to do is to work hard, get some relevant Microsoft DA-100 exam preparation material and go thoroughly from them.

Compared to other visualization tools we would recommend that Power BI is quite user friendly and is relatively easy to learn. What also contributes to this is that the Power BI desktop version is available for free, resulting in a pretty large user base on the internet, which is beneficial to your learning curve, as in our experience answers to particular questions can be found easily most of the times.

Exam DA-100 Topics Covered

Test DA-100 measures the entrant's skills in 5 different domains:

• Preparation of data (20-25%)

  The first domain for DA-100 exam consists of questions that will test the candidate's understanding of data retrieval from various sources. Thus, applicants should be able to identify the data source and connect to it, modify the source settings, select and create various data sets, pick a storage mode, etc. In addition, the examinee should be familiar with the concept of data profiling and have to be able to identify anomalies, study data structures, and examine column properties as well as data statistics. This section also includes cleaning, transforming, and loading data. This means that the entrant must know how to resolve inconsistencies, transform column data types, combine queries, solve data import errors, and more. This topic will also include Microsoft Dataverse, the usage & creation of a PBIDS file, and data flow after the changes are incorporated.

• Analyzing the data (10-15%)

  In the fourth tested area, the candidate will be asked to enhance reports for exposing insights. This means complying with the need to be well-informed about conditional formatting, slicers, filters, Analytics pane along with a few other concepts. Besides, candidates must be able to add Quick Insights results to reports and use Q&A visuals. Likewise, the exam-taker should be able to know how to execute the advanced analysis. This involves the understanding of outliers, Timer Series analysis, groupings, binning, dimensional variances, decompositions tree visuals, and AI Insights. The exam also requires an understanding of the Play Axis feature and the personalization of visuals.

• Visualizing the data (20-25%)

  The third domain will test the applicant's know-how of creating reports, dashboards, and enriching reports for usability. This will require the candidate's expertise in adding visualization to reports, choosing a proper visualization style, formatting visualizations, slicing and filtering applications, configuring a report page, building automatic page refresh, etc. The knowledge of R and Python visuals will also be beneficial. Furthermore, it is also essential for the entrant to know how to set dashboard to mobile view, manage tiles, configure data alerts, arrange aspects of the dashboard, and pin live reports. Finally, the applicant needs to enrich the reports by configuring bookmarks, creating custom tooltips, sorting & configuring various Sync Slicers, designing reports for mobile devices, etc.

• Modeling the data (25-30%)

  This part of the final exam includes designing a data model. Here, the candidate should be able to define and configure tables along with having an idea about various dimensions and relationships. They are also required to be familiar with the concept of cardinality and cross-filter direction. After designing a data model, the applicant should be aware of how to develop it. Therefore, understanding of security filtering, calculated tables, hierarchies, and the implementation of row-level security roles will be beneficial. Furthermore, the entrants' expertise in working with DAX will be tested in this sector. They are required to be capable of using DAX for building complex measures, taking advantage of CALCULATE for manipulating filters using DAX with Time Intelligence, and more. In addition, this portion of DA-100 also involves the optimization of model performance by removing rows and columns, changing data types, managing aggregations, etc. Finally, the entrant should also know how to create semi-additive measures.

• Deployment and maintenance deliverables (10-15%)

  The fifth and final section of DA-100 exam includes managing datasets. The candidate must be able to configure dataset scheduled refresh and row-level security. They are also expected to have an idea about giving access to data sets, incremental refresh settings, and certifying datasets. Moreover, the entrant is required to be well-informed about creating and managing workspaces. They should be able to create and configure workspaces, assign roles in the workspace, and manage the workspace app. Knowing how to import, export, update, and publish workspace assets will be essential. Finally, the candidate needs to know about applying sensitivity labels in workspace contents. The entrant also needs to be capable of identifying downstream data set dependencies and using deployment pipelines.

Skills measured

• Deploy and maintain deliverables (10-15%)
• Model the data (25-30%)
• Analyze the data (10-15%)
• Prepare the data (20-25%)
• Visualize the data (20-25%)

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Career Opportunities

The professionals with the Microsoft Certified: Data Analyst Associate certification are qualified for a variety of job titles. These include a Junior Data Analyst, an Associate Analyst, an Associate Consultant, a Certified Retail Analyst, a Technology Support Analyst, a Digital Media Analyst, and a Network Analyst. The average salary for these positions is $52,000 per annum.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/da-100

Microsoft DA-100日本語 Exam Syllabus Topics: