Useful Books to Pass DA-100

Those preparing for DA-100 test can incorporate the following books from Amazon in their learning process to ensure they pass the exam with high scores:

• ‘Exam DA-100: Data Analyst Associate 25 Test Prep Questions Version II’ by Ger Arevalo. This guide can act as supplementary material when going through hands-on practice, labs, and classes for DA-100. The intent of this book is to reinforce what you learn, and it is also a good last-minute tool that can help students identify their level of readiness. It will help candidates acquire competencies that will be beneficial when working out in the real world.
• ‘Exam Ref DA-100 Analyzing Data with Microsoft Power BI 1st Edition’ by Daniil Maslyuk. This is the official book issued by Microsoft for its DA-100 test. The guidance offered in this paperback aims to help candidates acquire all the right skills needed for the exam so they will be able to achieve a high score. The book puts great emphasis on real-world issues and problems so the readers will know what to expect when working in the job role of a data analyst. It provides complete coverage of all the tested objectives, and this content is extremely detailed. The material also includes various ‘what if’ scenarios to encourage critical thinking in students and prepare them for the more challenging exam questions.
• ‘Learn How to Pass DA-100: Analyzing Data with Microsoft Power BI Exam’. Prepared by Rodrezil Publications, this book compiles advice and tips from candidates who have already passed DA-100 exam and hence offers invaluable first-hand experiences. It also provides the readers with advice from IT experts who know all the ins and outs of the test and know how to deliver this knowledge in an easy-to-understand manner. The goal of this Kindle e-book is to help applicants get over the fear of the exam and pass it on their very first attempt. The material also comes with coupon codes that students can use to take free DA-100 mock tests at Vullam for better preparation and prior self-assessment.
• ‘Secrets of DA-100 Analyzing Data with Microsoft Power BI’ by Nikhil J. This Kindle e-book is packed with numerous up-to-date questions & answers for the Microsoft DA-100 exam in order to help students score better. By going through this material, the applicants will assess themselves and will understand how well they can apply all the things they learnt in theory. The e-book has a good track record of helping individuals achieve success in DA-100 test.

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

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This exam measures your ability to accomplish the following technical tasks: prepare the data; model the data; visualize the data; analyze the data; and deploy and maintain deliverables.

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Exam Topics

Microsoft expects that the successful candidates get the passing score of 720 points on a scale of 100-1000. This mark is achievable if you put your mind to it. To succeed, you must understand the Microsoft DA-100 exam topics that are as follows:

1. Data Preparation (20-25%):

This part includes the following areas:

• Collecting data from various data sources: The candidates must be able to connect and identify data sources, alternate data source settings, and choose storage mode and shared datasets, or build local datasets. They also need the capacity to select the relevant query type, utilize parameters, Microsoft Dataverse, create or use PBIDS files, and data flow.
• Cleaning, loading, & transforming data: This subject area requires that the students understand how to combine queries, utilize user-friendly value substitutions, solve data import errors, transform and measure column data types, and configure the data loading. It also covers their skills in applying data shape transformations for table structures and user-friendly conventional naming for queries and columns. The individuals should also have the expertise in leveraging Advanced Editor or modifying Power Query M-code.
• Data profile: The test takers should have competence in interrogating data statistics and column properties as well as identifying data anomalies and evaluating data structures.

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