Current DP-600 Exam Dumps [2024] Complete Microsoft Exam Smoothly
DP-600 Premium PDF & Test Engine Files with 87 Questions & Answers
NEW QUESTION # 23
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* The measure should use the AVERAGE function to calculate the average value.
* It should reference the Response Value column from the 'Survey' table.
* The 'Number of months' should be used to define the period for the average calculation.
To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.
NEW QUESTION # 24
You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields.
You need to reduce the memory used by Model! and the time it takes to refresh the model. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
- A. Replace TotalQuantity with a calculated column.
- B. Split OrderDateTime into separate date and time columns.
- C. Convert Quantity into the Text data type.
- D. Replace TotalSalesAmount with a measure.
Answer: A,D
Explanation:
To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don't need to be stored and adjusting data types to improve performance.
NEW QUESTION # 25
You have a Fabric tenant that contains two lakehouses.
You are building a dataflow that will combine data from the lakehouses. The applied steps from one of the queries in the dataflow is shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 26
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.
Which connection and endpoint should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used.
NEW QUESTION # 27
Which type of data store should you recommend in the AnalyticsPOC workspace?
- A. a lakehouse
- B. an external Hive metaStore
- C. a data lake
- D. a warehouse
Answer: A
NEW QUESTION # 28
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?
- A. From the Destination tab, set Mode to Append.
- B. From the Destination tab, select the partition column,
- C. From the Destination tab, set Mode to Overwrite.
- D. From the Source tab, select Enable partition discovery
Answer: C
Explanation:
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. References = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.
NEW QUESTION # 29
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run 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 code embeds an existing Power BI report. - No
* The code creates a Power BI report. - No
* The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it's likely part of a data processing step that summarizes data.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks
NEW QUESTION # 30
You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
- A. user-defined aggregations
- B. query caching
- C. OneLake integration
- D. automatic aggregation
Answer: A,D
Explanation:
User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.
NEW QUESTION # 31
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.
Which connection and endpoint should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 32
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.
Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS.
Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.
Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.
Based on these considerations, the selections should be:
* Authentication method: Service principal authentication
* Mode: DirectQuery
NEW QUESTION # 33
You need to implement the date dimension in the data store. The solution must meet the technical requirements.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Populate the date dimension table by using a Stored procedure activity in a pipeline.
- B. Populate the date dimension view by using T-SQL.
- C. Populate the date dimension table by using a Copy activity in a pipeline.
- D. Populate the date dimension table by using a dataflow.
Answer: A,D
Explanation:
Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.
NEW QUESTION # 34
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
* Hide the column.
* Set Nullable to False.
* Set Summarize By to None
* Set Available in MDX to False.
* Mark the column as a key column.
What should you use?
- A. Tabular Editor
- B. DAX Studio
- C. Microsoft Power Bl Desktop
- D. ALM Toolkit
Answer: A
Explanation:
Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:
* Open the model in Tabular Editor.
* Create an Advanced Script using C# to iterate over all tables and their respective columns.
* Within the script, check if the column name ends with 'Key'.
* For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.
* Additionally, mark the column as a key column.
* Save the changes and deploy them back to the Fabric tenant.
References: The ability to batch-edit properties using scripts in Tabular Editor is well-documented in the tool's official documentation and user community resources.
NEW QUESTION # 35
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. a KQL database
- C. a warehouse
- D. an Azure SQL database
Answer: A
Explanation:
A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.
NEW QUESTION # 36
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows. You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;
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:
* A replica of dbo.Sales is created in the test schema by copying the metadata only. - No
* Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No
* Additional data changes to dbo.FactSales will also apply to test.FactSales. - Yes The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated.
References =
* CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse
NEW QUESTION # 37
You have a Fabric tenant that contains a Microsoft Power Bl report.
You are exploring a new semantic model.
You need to display the following column statistics:
* Count
* Average
* Null count
* Distinct count
* Standard deviation
Which Power Query function should you run?
- A. Table.Profile
- B. Table. FuzzyGroup
- C. Table.Schema
- D. Table.View
Answer: A
Explanation:
The Table.Profile function in Power Query is used to generate column statistics such as count, average, null count, distinct count, and standard deviation. You can use this function as follows:
* Invoke the Power Query Editor.
* Apply the Table.Profile function to your table.
* The result will be a table where each row represents a column from the original table, and each column in the result represents a different statistic such as those listed in the requirement.
References: The use of Table.Profile is part of Power Query M function documentation where it explains how to gather column statistics for a given table.
NEW QUESTION # 38
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. DAX EC.
- B. PySpark
- C. T-SQL
- D. Spark SQL
Answer: B,D
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.
NEW QUESTION # 39
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
- A. Query the discover_hehory6Rant dynamic management view (DMV).
- B. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).
- C. Use the Vertipaq Analyzer tool.
- D. Use the Analyze in Excel feature.
Answer: B,C
NEW QUESTION # 40
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value 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.
Answer:
Explanation:
Explanation:
You should complete the code as follows:
* Table
* NativeQuery
* EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).
NEW QUESTION # 41
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.
What can you identify about the pickupLongitude column?
- A. All the table rows are profiled.
- B. There are 935 values that occur only once.
- C. The column has missing values.
- D. The column has duplicate values.
Answer: A
Explanation:
The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. References = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.
NEW QUESTION # 42
You have the source data model shown in the following exhibit.
The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* 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
NEW QUESTION # 43
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.
Which code should you run to populate the results DataFrame?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame ( customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.
NEW QUESTION # 44
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._connections
- B. sys.dn_.exec._sessions
- C. sys.dm-exec_requests
- D. sys.dm_pdw_exec_requests
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.
NEW QUESTION # 45
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. write
- B. display
- C. displayMTML
- D. show
Answer: B
NEW QUESTION # 46
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