Implementing Analytics Solutions Using Microsoft Fabric (DP-600) Practice Exam
Implementing Analytics Solutions Using Microsoft Fabric (DP-600) Practice Exam
About Microsoft Fabric (DP-600) Exam
Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam is suitable for candidates having subject matter expertise to design, build, and deploy enterprise-scale data analytics solutions. Candidates will be required to implement analytics best practices in Microsoft Fabric like version control and deployment.
Roles and Responsibilities
Candidates taking the exam will be responsible for transforming data into reusable analytics assets by using Microsoft Fabric components, including:
- Lakehouses
- Data warehouses
- Notebooks
- Dataflows
- Data pipelines
- Semantic models and Reports
Job Roles
Candidates planning to take the exam will be required to implement solutions as a Fabric analytics engineer, partnering with other roles, such as:
- Solution Architects
- Data Engineers
- Data Scientists
- AI Engineers
- Database Administrators
- Power BI data analysts
Experience Required
Candidates are required to have in-depth work with the Fabric platform, candidates need experience with -
- Data modeling
- Data transformation
- Git-based source control
- Exploratory analytics
Candidates must also have knowledge of languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark
Course Outline
The Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam covers the latest and updated topics -
Module 1- Describe planning, executing and managing solution for data analytics (10–15%)
1.1 Explain planning a data analytics environment
- Learn to understand requirements for a solution, like components, features, performance, and capacity stock-keeping units (SKUs)
- Learn to suggest settings in the Fabric admin portal
- Learn to select a data gateway type
- Learn to build a custom Power BI report theme
- Learn to execute and manage a data analytics environment
- Learn to execute workspace and item-level access controls for Fabric items
- Learn to execute data sharing for workspaces, warehouses, and lakehouses
- Learn to manage sensitivity labels in semantic models and lakehouses
- Learn to configure Fabric-enabled workspace settings
- Learn to handle Fabric capacity
1.2 Explain managing analytics development lifecycle
- Learn to execute version control for a workspace
- Learn to build and manage a Power BI Desktop project (.pbip)
- Learn to plan and execute deployment solutions
- Learn to perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models
- Learn to deploy and manage semantic models by using the XMLA endpoint
- Learn to build and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models
Module 2 - Describe to prepare and serve data (40–45%)
2.1 Explain to build objects in a lakehouse or warehouse
- Learn to data ingestion by using a data pipeline, dataflow, or notebook
- Learn to build and manage shortcuts
- Learn to apply file partitioning for analytics workloads in a lakehouse
- Learn to create views, functions, and stored procedures
- Learn to data enrichment by adding new columns or tables
2.2 Explain Copy data
- Learn to select suitable method for copying data from a Fabric data source to a lakehouse or warehouse
- Learn to data copying by using a data pipeline, dataflow, or notebook
- Learn adding stored procedures, notebooks, and dataflows to a data pipeline
- Learn scheduling data pipelines
- Learn scheduling dataflows and notebooks
2.3 Explain Data Transformation
- Learn to apply data cleansing process
- Learn to apply a star schema for a lakehouse or warehouse (Type 1 and Type 2 slowly changing dimensions)
- Learn to apply bridge tables for a lakehouse or a warehouse
- Learn to denormalize data
- Learn to use aggregate or de-aggregate data
- Learn to merge or join data
- Learn to identify and resolve duplicate data, missing data, or null values
- Learn to data conversion types by using SQL or PySpark
- Learn to filter data
2.4 Explain performance optimization
- Learn to determine and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries
- Learn to apply performance improvements in dataflows, notebooks, and SQL queries
- Learn to determine and resolve issues with Delta table file sizes
Module 3- Describe implementing and managing semantic models (20–25%)
3.1 Explain designing and building semantic models
- Learn tot select a storage mode, including Direct Lake
- Learn to determine use cases for DAX Studio and Tabular Editor 2
- Learn to apply a star schema for a semantic model
- Learn to implement relationships (including bridge tables and many-to-many relationships)
- Learn to write calculations that use DAX variables and functions (including iterators, table filtering, windowing, and information functions)
- Learn to implement calculation groups, dynamic strings, and field parameters
- Learn to design and build a large format dataset
- Learn to design and build composite models that include aggregations
- Learn to apply dynamic row-level security and object-level security
- Learn to evaluate row-level security and object-level security
3.2 Explain optimization of enterprise-scale semantic models
- Learn to apply performance improvements in queries and report visuals
- Learn to enhance DAX performance by using DAX Studio
- Learn to optimize a semantic model by using Tabular Editor 2
- Learn to implement incremental refresh
Module 4 - Describe to Data exploration and Analysis (20–25%)
4.1 Explain performing exploratory analytics
- Learn to apply descriptive and diagnostic analytics]
- Learn to integrate prescriptive and predictive analytics into a visual or report
- Learn to profile data
4.2 Explain Query data by using SQL
- Learn to query a lakehouse in Fabric by using SQL queries or the visual query editor
- Learn to query a warehouse in Fabric by using SQL queries or the visual query editor
- Learn to connect to and query datasets by using the XMLA endpoint
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- In-depth and exhaustive explanation for every question
- Reliable exam reports to evaluate strengths and weaknesses
- Latest Questions with an updated version
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What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real time exam scenario.
- Practice exam dumps have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
