DP-600: Implementing Analytics Solutions Using Microsoft Fabric (beta)

Question #: 40
Topic #: 1
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
Support the use of dataflows to load and append data to the data store.
Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

A. a lakehouse
B. an Azure SQL database
C. a warehouse
D. a KQL database

Selected Answer: A

Question #: 42
Topic #: 1
You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the data columns into attribute-value pairs, where columns become rows.
You select the VendorID column.
Which transformation should you select from the context menu of the VendorID column?

A. Group by
B. Unpivot columns
C. Unpivot other columns
D. Split column
E. Remove other columns

Selected Answer: C

Question #: 43
Topic #: 1
You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?

A. Daily
B. By the minute
C. Weekly
D. Hourly

Selected Answer: C

Question #: 44
Topic #: 1
You have a Fabric tenant that contains a warehouse.
Several times a day, the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.
What should you use to identify whether throttling is occurring?

A. the Capacity settings
B. the Monitoring hub
C. dynamic management views (DMVs)
D. the Microsoft Fabric Capacity Metrics app

Selected Answer: D

Question #: 46
Topic #: 1
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?

A. sys.dm_exec_requests
B. sys.dm_exec_sessions
C. sys.dm_exec_connections
D. sys.dm_pdw_exec_requests

Selected Answer: A

Question #: 49
Topic #: 1
You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

A. Update rows when the non-key attributes have changed.
B. Insert new rows when the natural key exists in the dimension table, and the non-key attribute values have changed.
C. Update the effective end date of rows when the non-key attribute values have changed.
D. Insert new records when the natural key is a new value in the table.

Selected Answer: AD

Question #: 51
Topic #: 1
You are analyzing customer purchases in a Fabric notebook by using PySpark.
You have the following DataFrames:
transactions: Contains five columns named transaction_id, customer_id, product_id, amount, and date and has 10 million rows, with each row representing a transaction. customers: Contains customer details in 1,000 rows and three columns named customer_id, name, and country.
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling.
You write the following code.
from pyspark.sql import functions as F
results =
Which code should you run to populate the results DataFrame?

A. transactions.join(F.broadcast(customers), transactions.customer_id == customers.customer_id)
B. transactions.join(customers, transactions.customer_id == customers.customer_id).distinct()
C. transactions.join(customers, transactions.customer_id == customers.customer_id)
D. transactions.crossJoin(customers).where(transactions.customer_id == customers.customer_id)

Selected Answer: A

Question #: 54
Topic #: 1
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 new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?

A. Yes
B. No

Selected Answer: B

Question #: 55
Topic #: 1
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 new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.show()
Does this meet the goal?

A. Yes
B. No

Selected Answer: B

Question #: 56
Topic #: 1
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 new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.summary()
Does this meet the goal?

A. Yes
B. No

Selected Answer: A

Question #: 56
Topic #: 1
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 new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.summary()
Does this meet the goal?

A. Yes
B. No

Selected Answer: A

Question #: 57
Topic #: 1
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 lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:

DESCRIBE HISTORY customer –
Does this meet the goal?

A. Yes
B. No

Selected Answer: A

Question #: 58
Topic #: 1
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 lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:

REFRESH TABLE customer –
Does this meet the goal?

A. Yes
B. No

Selected Answer: B

Question #: 59
Topic #: 1
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 lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:

EXPLAIN TABLE customer –
Does this meet the goal?

A. Yes
B. No

Selected Answer: B

Question #: 60
Topic #: 1
Case study –

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.

To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study –
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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 recommend a solution to prepare the tenant for the PoC.

Which two actions should you recommend performing from the Fabric Admin portal? Each correct answer presents part of the solution.

NOTE: Each correct answer is worth one point.

A. Enable the Users can try Microsoft Fabric paid features option for the entire organization.
B. Enable the Users can try Microsoft Fabric paid features option for specific security groups.
C. Enable the Allow Azure Active Directory guest users to access Microsoft Fabric option for specific security groups.
D. Enable the Users can create Fabric items option and exclude specific security groups.
E. Enable the Users can create Fabric items option for specific security groups.

Selected Answer: BE