DP-600 – Q&A PDF Exam

Description

About the exam Languages Some exams are localized into other languages. You can find these in the Schedule Exam section of the Exam Details webpage. If the exam isn’t available in your preferred language, you can request an additional 30 minutes to complete the exam. Note The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam. Note Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used. Skills measured Audience profile As a candidate for this exam, you should have subject matter expertise in designing, creating, and deploying enterprise-scale data analytics solutions. Your responsibilities for this role include transforming data into reusable analytics assets by using Microsoft Fabric components, such as: Lakehouses Data warehouses Notebooks Dataflows Data pipelines Semantic models Reports You implement analytics best practices in Fabric, including version control and deployment. To implement solutions as a Fabric analytics engineer, you partner with other roles, such as: Solution architects Data engineers Data scientists AI engineers Database administrators Power BI data analysts In addition to in-depth work with the Fabric platform, you need experience with: Data modeling Data transformation Git-based source control Exploratory analytics Languages, including Structured Query Language (SQL), Data Analysis Expressions (DAX), and PySpark Skills at a glance Plan, implement, and manage a solution for data analytics (10–15%) Prepare and serve data (40–45%) Implement and manage semantic models (20–25%) Explore and analyze data (20–25%) Plan, implement, and manage a solution for data analytics (10–15%) Plan a data analytics environment Identify requirements for a solution, including components, features, performance, and capacity stock-keeping units (SKUs) Recommend settings in the Fabric admin portal Choose a data gateway type Create a custom Power BI report theme Implement and manage a data analytics environment Implement workspace and item-level access controls for Fabric items Implement data sharing for workspaces, warehouses, and lakehouses Manage sensitivity labels in semantic models and lakehouses Configure Fabric-enabled workspace settings Manage Fabric capacity Manage the analytics development lifecycle Implement version control for a workspace Create and manage a Power BI Desktop project (.pbip) Plan and implement deployment solutions Perform impact analysis of downstream dependencies from lakehouses, data warehouses, dataflows, and semantic models Deploy and manage semantic models by using the XMLA endpoint Create and update reusable assets, including Power BI template (.pbit) files, Power BI data source (.pbids) files, and shared semantic models Prepare and serve data (40–45%) Create objects in a lakehouse or warehouse Ingest data by using a data pipeline, dataflow, or notebook Create and manage shortcuts Implement file partitioning for analytics workloads in a lakehouse Create views, functions, and stored procedures Enrich data by adding new columns or tables Copy data Choose an appropriate method for copying data from a Fabric data source to a lakehouse or warehouse Copy data by using a data pipeline, dataflow, or notebook Add stored procedures, notebooks, and dataflows to a data pipeline Schedule data pipelines Schedule dataflows and notebooks Transform data Implement a data cleansing process Implement a star schema for a lakehouse or warehouse, including Type 1 and Type 2 slowly changing dimensions Implement bridge tables for a lakehouse or a warehouse Denormalize data Aggregate or de-aggregate data Merge or join data Identify and resolve duplicate data, missing data, or null values Convert data types by using SQL or PySpark Filter data Optimize performance Identify and resolve data loading performance bottlenecks in dataflows, notebooks, and SQL queries Implement performance improvements in dataflows, notebooks, and SQL queries Identify and resolve issues with Delta table file sizes Implement and manage semantic models (20–25%) Design and build semantic models Choose a storage mode, including Direct Lake Identify use cases for DAX Studio and Tabular Editor 2 Implement a star schema for a semantic model Implement relationships, such as bridge tables and many-to-many relationships Write calculations that use DAX variables and functions, such as iterators, table filtering, windowing, and information functions Implement calculation groups, dynamic strings, and field parameters Design and build a large format dataset Design and build composite models that include aggregations Implement dynamic row-level security and object-level security Validate row-level security and object-level security Optimize enterprise-scale semantic models Implement performance improvements in queries and report visuals Improve DAX performance by using DAX Studio Optimize a semantic model by using Tabular Editor 2 Implement incremental refresh Explore and analyze data (20–25%) Perform exploratory analytics Implement descriptive and diagnostic analytics Integrate prescriptive and predictive analytics into a visual or report Profile data Query data by using SQL Query a lakehouse in Fabric by using SQL queries or the visual query editor Query a warehouse in Fabric by using SQL queries or the visual query editor Connect to and query datasets by using the XMLA endpoint Sample Questions and Answers QUESTION 1 What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace? A. a stored procedure B. a pipeline that contains a KQL activity C. a Spark notebook D. a dataflow Answer: D QUESTION 2 Which type of data store should you recommend in the AnalyticsPOC workspace? A. a data lake B. a warehouse C. a lakehouse D. an external Hive metaStore Answer: C QUESTION 3 You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables: Table1: A Delta table created by using a shortcut Table2: An external table created by using Spark Table3: A managed table Answer: A QUESTION 4 You plan to connect to Lakehouse1 by using its SQL endpoint. What will you be able to do after connecting to Lakehouse1? A. ReadTable3. B. Update the data Table3. C. ReadTable2. D. Update the data in Table1. Answer: D QUESTION 5 You have a Fabric tenant that contains a warehouse. You use a dataflow to load a new dataset from OneLake to the warehouse. You need to add a Power Query step to identify the maximum values for the numeric columns. Which function should you include in the step? A. Table. MaxN B. Table.Max C. Table.Range D. Table.Profile Answer: B QUESTION 6 You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the predict function in a fabric notebook. Which two languages can you use to perform model scoring? Each correct answer presents a complete solution. NOTE: Each correct answer is worth one point. A. T-SQL B. DAX EC. C. Spark SQL D. PySpark Answer: C, D QUESTION 7 You are analyzing the data in a Fabric notebook. You have a Spark DataFrame assigned to a variable named df. You need to use the Chart view in the notebook to explore the data manually. Which function should you run to make the data available in the Chart view? 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