Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric
| Exam Number: DP-600 | Length of test: 120 mins |
| Exam Name: Implementing Analytics Solutions Using Microsoft Fabric | Number of questions in the actual exam: 40-60 |
| Format: PDF, VPLUS | Passing Score: 700/1000 |
|
Total Questions: 112 $30 Premium PDF file 2 months updates Last updated: November-2024 |
Total Questions: 112 FREE Premium VPLUS file Last updated: November-2024 |
Download practice test questions
| Title | Size | Hits | Download |
|---|---|---|---|
| Microsoft.DP-600.vAug-2024.by.Locyn.37q | 748.38 KB | 61 | Download |
| Microsoft.DP-600.vAug-2024.by.Locyn.37q | 633.85 KB | 67 | Download |
| Microsoft.DP-600.by.Rian.30q | 1.71 MB | 61 | Download |
| Microsoft.DP-600.by.Rian.30q | 1.73 MB | 45 | Download |
Study guide for Exam DP-600: Implementing Analytics Solutions Using Microsoft Fabric
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
- Programming 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
- Implement and manage a data analytics environment
- Manage the analytics development lifecycle
Prepare and serve data (40–45%)
- Create objects in a lakehouse or warehouse
- Copy data
- Transform data
- Optimize performance
Implement and manage semantic models (20–25%)
- Design and build semantic models
- Optimize enterprise-scale semantic models
Explore and analyze data (20–25%)
- Perform exploratory analytics
- Query data by using SQL
Some new questions:
Q
What should you use to implement calculation groups for the Research division semantic models?
A. DAX Studio
B. Microsoft Power Bl Desktop
C. the Power Bl service
D. Tabular Editor
Q
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
In Workspace1. you create a data pipeline named Pipeline1.
You have CSV files stored in an Azure Storage account.
You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1. The activity must support Power Query M formula language expressions.
Which type of activity should you add?
A. Dataflow
B. Notebook
C. Copy data
D. Script
Q
HOTSPOT
You need to migrate the Research division data for Productline2. The solution must meet the data preparation requirements. How should you complete the code? To answer, select the appropriate options in the answer area
Q
You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse! contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?
A. a view
B. a dataflow
C. a managed table
D. a shortcut
……..
Some new questions:
Q
You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake. The data contains details about customer demographics and product usage.
You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age. and customer tenure.
Which type of analytics are you performing?
A. prescriptive
B. diagnostic
C. descriptive
D. predictive
Q
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
Does this meet the goal?
A. Yes
B. No
Q
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
Does this meet the goal?
A. Yes
B. No
Q
You have a Fabric tenant that contains a lakehouse. You plan to use a visual query to merge two tables.
You need to ensure that the query returns all the rows that are present in both tables. Which type of join should you use?
A. left outer
B. right anti
C. full outer
D. left anti
E. right outer
F. inner
Q
HOTSPOT
You have a Fabric tenant that contains lakehouse named Lakehousel. Lakehousel contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns.
You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements:
* Keep all the existing rows.
* Ensure that all the new data is added to the table.
How should you complete the code?
……………