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NEW QUESTION # 46
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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
NEW QUESTION # 47
You have the following T-SQI statement.
Answer:
Explanation:
Explanation:
The statement uses SUM(CASE WHEN RefundStatus = ' Refunded ' THEN SalesAmount ELSE 0 END) AS TotalRevenue, which calculates TotalRevenue by summing SalesAmount only when RefundStatus is ' Refunded ' , and 0 otherwise. This means Region values are returned regardless of RefundStatus, but the TotalRevenue reflects refunded items.
The WHERE (TransactionDate) - YEAR(GETDATE()) condition is incomplete and lacks proper comparison (e.g., no equality or range check), so it does not filter for the current year.
The TotalRevenue calculation does not aggregate all SalesAmount values with RefundStatus of ' Refunded ' ; it sums SalesAmount only for refunded items within the grouped data, with 0 for non-refunded items.
NEW QUESTION # 48
You have a Microsoft Power BI report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.
What will provide the greatest reduction in the rendering duration of Report1?
Answer: D
Explanation:
https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-performance-analyzer#display- the-performance-analyzer-pane Other - This is the time required by the visual for preparing queries, waiting for other visuals to complete, or performing other background processing.
NEW QUESTION # 49
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().show()
Does this meet the goal?
Answer: A
Explanation:
df.explain gives execution plan.
https://spark.apache.org/docs/3.1.2/api/python/reference/api/pyspark.sql.DataFrame.explain.html
NEW QUESTION # 50
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 Lakehousel. Lakehousel 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 DETAIL customer
Does this meet the goal?
Answer: A
Explanation:
The command:
DESCRIBE DETAIL Customer
Returns metadata about the Delta table, such as format, schema, partitioning, size, number of files, and creation/modification timestamps.
However, it does not provide information about whether maintenance tasks (such as OPTIMIZE, VACUUM, or Z-Ordering) were performed on the table.
To check maintenance history, you would typically use:
DESCRIBE HISTORY Customer
which shows operations executed on the table (e.g., OPTIMIZE, VACUUM, MERGE).
Since DESCRIBE DETAIL does not satisfy the requirement, the solution does not meet the goal.
Reference:
DESCRIBE DETAIL in Delta Lake
DESCRIBE HISTORY in Delta Lake
NEW QUESTION # 51
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