You have a Fabric workspace that contains a semantic model named Model1.
You need to monitor the refresh history of Model1 and visualize the refresh history in a chart.
What should you use?
A. the refresh history from the settings of Model1
B. a notebook
C. a Dataflow Gen2 dataflow
D. a data pipeline
Show Answer
Correct Answer: B
Explanation: A semantic model's built-in refresh history shows a tabular log but does not provide chart visualizations. A Fabric notebook can retrieve refresh history (for example via APIs) and use Python libraries to visualize it as charts, satisfying both the monitoring and visualization requirements.
Question 42
HOTSPOT
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You plan to process the following three datasets by using Fabric:
Dataset1: This dataset will be added to Fabric and will have a unique primary key between the source and the destination. The unique primary key will be an integer and will start from 1 and have an increment of 1.
Dataset2: This dataset contains semi-structured data that uses bulk data transfer. The dataset must be handled in one process between the source and the destination. The data transformation process will include the use of custom visuals to understand and work with the dataset in development mode.
Dataset3: This dataset is in a lakehouse. The data will be bulk loaded. The data transformation process will include row-based windowing functions during the loading process.
You need to identify which type of item to use for the datasets. The solution must minimize development effort and use built-in functionality, when possible.
What should you identify for each dataset? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Dataset1: Dataflow Gen2 dataflow
Dataset2: A notebook
Dataset3: A T-SQL statement
Explanation: Dataflow Gen2 provides built-in low-code ingestion and can preserve an existing integer primary key with minimal effort. Notebooks are best for semi-structured bulk processing and interactive/custom visual exploration during development. T-SQL natively supports window functions (such as ROW_NUMBER and other OVER clauses) for transformations during bulk loading from a lakehouse.
Question 43
You have a Fabric workspace that contains a warehouse named Warehouse1.
While monitoring Warehouse1, you discover that query performance has degraded during the last 60 minutes.
You need to isolate all the queries that were run during the last 60 minutes. The results must include the username of the users that submitted the queries and the query statements.
What should you use?
A. the Microsoft Fabric Capacity Metrics app
B. views from the queryinsights schema
C. Query activity
D. the sys.dm_exec_requests dynamic management view
Show Answer
Correct Answer: B
Explanation: The queryinsights schema provides historical query execution views (such as exec_requests_history) that can be queried for a custom time window like the last 60 minutes and include fields such as the login/user and the SQL statement. Capacity Metrics is capacity-level monitoring, sys.dm_exec_requests only shows currently running requests, and Query activity is a UI for monitoring but the requirement to isolate queries from the last 60 minutes with query text and usernames is best met by querying the queryinsights history views.
Question 44
HOTSPOT
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You need to recommend a Fabric streaming solution that will use the sources shown in the following table.
The solution must minimize development effort.
What should you include in the recommendation for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: Eventstream has practical message-size limits far below 5–25 MB, data pipelines are batch (not streaming), and streaming dataflows are not appropriate here. Spark Structured Streaming supports large messages and unstructured/binary content with the least viable development effort among the given streaming options.
Question 45
You have a Fabric warehouse named DW1 that contains a Type 2 slowly changing dimension (SCD) dimension table named DimCustomer. DimCustomer contains 100 columns and 20 million rows. The columns are of various data types, including int, varchar, date, and varbinary.
You need to identify incoming changes to the table and update the records when there is a change. The solution must minimize resource consumption.
What should you use to identify changes to attributes?
A. a hash function to compare the attributes in the source table.
B. a direct attributes comparison across the attributes in the DimCustomer table.
C. a direct attributes comparison for the attributes in the source table.
D. a hash function to compare the attributes in the DimCustomer table.
Show Answer
Correct Answer: A
Explanation: For a very large Type 2 SCD with 100 columns and 20 million rows, computing and comparing a hash of the business attributes is far more efficient than performing direct comparisons across every column. The hash is typically generated from the incoming source attributes and compared with the corresponding stored value or recomputed target hash to detect changes, minimizing comparison cost.
Question 46
HOTSPOT
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You have a Fabric warehouse named DW1 that contains four staging tables named ProductCategory, ProductSubcategory, Product, and SalesOrder. ProductCategory, ProductSubcategory, and Product are used often in analytical queries.
You need to implement a star schema for DW1. The solution must minimize development effort.
Which design approach should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Denormalized into a single product dimension table
The unique system generated identifier
Explanation: A star schema minimizes analytical complexity by flattening the product hierarchy into one dimension table. Fact-to-dimension joins should use a stable surrogate (system-generated) key rather than descriptive attributes.
Question 47
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 publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source:
• Sales Date
• Author
• Price
• Units
• SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.
Existing Environment. Security Groups
Litware has the following security groups:
• Sales
• Fabric Admins
• Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: “The SQL query failed while running.”
The data engineering team wants to debug the issue and find queries that cause more than one failure.
When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process.
The company’s sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning.
Requirements. Planned Changes -
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API.
Requirements. Version Control -
Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned.
Requirements. Data Requirements -
Litware identifies the following data requirements:
• Process the SEO data in near-real-time (NRT).
• Make the book reviews available in the lakehouse without making a copy of the data.
• When a new book cover image arrives in the Files folder, process the image as soon as possible.
You need to create a workflow for the new book cover images.
Which two components should you include in the workflow? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. a time-based schedule
B. a streaming dataflow
C. a blob storage action
D. a data pipeline
E. a notebook that uses Apache Spark Structured Streaming
F. a reflex item
Show Answer
Correct Answer: D, F
Explanation: Use a Fabric data pipeline to orchestrate the workflow, and a Reflex (now Activator) item for event-driven execution when new data arrives. This satisfies the requirement to process new images as soon as possible using Fabric-native components. A time schedule is not immediate, streaming dataflows are for streaming data rather than file arrival, Structured Streaming is not the event trigger for file arrival, and blob storage action refers to an action rather than the Fabric workflow components requested.
Question 48
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on-premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?
A. a data pipeline
B. an Apache Spark job definition
C. a streaming dataflow
D. a notebook
Show Answer
Correct Answer: A
Explanation: A data pipeline is the appropriate Fabric item for copying data from an on-premises SQL Server database to a Fabric warehouse through an on-premises data gateway. Pipelines provide built-in copy activities and support gateway-based connectivity for on-premises data sources. Apache Spark job definitions and notebooks are intended for compute and custom processing rather than gateway-based ingestion, while streaming dataflows are designed for streaming scenarios rather than batch copy operations.
Question 49
DRAG DROP
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You have a Fabric workspace that contains an eventhouse named Eventhouse1.
In Eventhouse1, you plan to create a table named DeviceStreamData in a KQL database. The table will contain data based on the following sample.
You need to use a KQL query to develop the solution for Eventhouse1.
Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
Explanation: Create the table first, define Timestamp and DeviceId with their scalar types, and store the JSON array in StreamData as a dynamic column. The table name in the prompt says DeviceStreamData, but the provided code segment uses EventStreamData.
Question 50
HOTSPOT
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You have a table in a Fabric lakehouse that contains the following data.
You have a notebook that contains the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Show Answer
Correct Answer: Yes
No
No
Explanation: Line 1 replaces null or empty CustomerName values with "Unknown". Line 2 uses split(...).getItem(1), which returns the part after '@' (the domain), not the username before '@'. Line 3 removes duplicates by full OrderDate, then extracts the year, so it does not keep only the first occurrence for each year.
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