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Implementing Analytics Solutions Using Microsoft Fabric
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QUESTION 1
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 .sumary () Does this meet the goal? A. Yes B. No Correct Answer: A Explanation: Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. References = The PySpark API documentation details the summary() function and the statistics it provides.
QUESTION 2 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 Correct Answer: CD Explanation: The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.
QUESTION 3 You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
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Correct Answer:
In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).
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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
QUESTION 5 You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1. You are creating a new data pipeline. You plan to copy external data to Table1. The schema of the external data changes regularly. You need the copy operation to meet the following requirements: Replace Table1 with the schema of the external data. Replace all the data in Table1 with the rows in the external data. You add a Copy data activity to the pipeline. What should you do for the Copy data activity? A. From the Source tab, add additional columns. B. From the Destination tab, set Table action to Overwrite. C. From the Settings tab, select Enable staging D. From the Source tab, select Enable partition discovery E. From the Source tab, select Recursively Correct Answer: B Explanation: For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table. References = Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.
QUESTION 6 You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.
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Hot Area:
Correct Answer:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context. References = DataFrame write partitionBy Apache Spark optimization with partitioning
QUESTION 8
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.dn_.exec._sessions
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C. sys.dm._exec._connections D. sys.dm_pdw_exec_requests Correct Answer: D Explanation: The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.
QUESTION 9 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 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. Correct Answer: D
QUESTION 10
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? A. displayMTML B. show C. write D. display
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Correct Answer:
The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns The Company entity must be: Denormalized into the Customer and Product entities In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes. References = Dimensional modeling Star schema design
QUESTION 12
You have a Fabric tenant that contains a lakehouse named Lakehouse Readings from 100 loT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily. All the table and SparkSession settings are set to the default. You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used. You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.
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What should you do? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Select and Place:
Correct Answer:
Remove the files: Run the VACUUM command on a schedule. Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule. To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.
QUESTION 13 You have a Fabric workspace named Workspace1 and an Azure Data Lake Storage Gen2 account named storage"!. Workspace1 contains a lakehouse named Lakehouse1. You need to create a shortcut to storage! in Lakehouse1.
