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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Analytics Solutions Using Microsoft Fabric |
| Exam Number: | DP-600 |
| Real Exam Qty: | 40–60 |
| Exam Format: | Case study, Scenario-based, Multiple choice |
| Certificate Validity Period: | 2 years |
| Available Languages: | Chinese (Simplified), English, Spanish, Portuguese (Brazil), German, French, Japanese |
| Passing Score: | 700 / 1000 |
| Exam Price: | USD 165 |
| Exam Duration: | 100 minutes |
| Recommended Training: | Microsoft Learn: DP-600 Learning Path |
| Exam Registration: | Microsoft Certification Exam Registration |
| Sample Questions: | Microsoft DP-600 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience with data modeling, SQL, DAX, and Microsoft Fabric components |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-600 |
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NEW QUESTION # 73
You have a data warehouse that contains a table named Stage. Customers. Stage-Customers contains all the customer record updates from a customer relationship management (CRM) system. There can be multiple updates per customer You need to write a T-SQL query that will return the customer ID, name, postal code, and the last updated time of the most recent row for each customer ID.
How should you complete the code? To answer, select the appropriate options in the answer area, NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).
* In the WHERE clause, choose WHERE X = 1.
To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number (X) is 1, you get the latest update per customer.
References =
* Use the OVER clause to aggregate data per partition
* Use window functions
NEW QUESTION # 74
You need to create a Microsoft Power BI file that will be used to create multiple reports. The solution must meet the following requirements:
- The file must include predefined data source connections.
- The file must include the report structure and formatting.
- The file must NOT contain any data.
Which file format should you use?
Answer: A
NEW QUESTION # 75
You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time?
Answer: B
Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
References: This information is based on the DAX reference and performance optimization guidelines in the Microsoft Power BI documentation.
NEW QUESTION # 76
Case Study 2 - Litware, Inc
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
The customer satisfaction data contains the following tables:
- Survey
- Question
- Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
- A custom semantic model
- A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers' discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do?
Answer: C
Explanation:
Pipeline can ensure the activities follow the required sequence.
https://learn.microsoft.com/en-us/fabric/data-factory/activity-overview#data-transformation- activities
NEW QUESTION # 77
You have a Fabric eventhouse named Eventhousel that contains a table named Weatherdata. A sample of the data in Weatherdata is shown in the following table.
You plan to use a KQL queryset to manipulate the data in Eventhouse1. The result set must have the following columns.
You need to build the query.
Which four KQL statements should you use in sequence? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 78
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2026 Latest iPassleader DP-600 PDF Dumps and DP-600 Exam Engine Free Share: https://drive.google.com/open?id=1UkzKU81_NQ7YAW7EG-FwSBn3b6NFtRDM