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질문 # 118
Hotspot Question
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1.2.
You plan to read a CSV file named Sales_raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have 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.
정답：
설명：
질문 # 119
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.
정답：A,D
설명：
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.
Topic 1, Litware. Inc. Case Study
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
litware has been using a Microsoft Power Bl tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Fabric Environment
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.
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 Litware 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, dataflows, 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-SQL 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 pnces 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 Bl 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
* Data Engineers: The data engineers on the analytics team
* Analytics Engineers: The analytics engineers on the analytics team
Report Requirements
The data analysis 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 date
* 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
질문 # 120
You have a Fabric tenant that contains lakehouse named Lakehouse1. Lakehouse1 contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns.
You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements:
* Keep all the existing rows.
* Ensure that all the new data is added to the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
정답：
설명：
Explanation:
o add new data to the Delta table while meeting the specified requirements:
You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.
You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.
The completed code would look like this:
df.write.format( " delta " ).mode( " append " )
option( " mergeSchema " , " true " )
saveAsTable( " Lakehouse1.TableName " )
질문 # 121
Shortcuts in OneLake are objects that link to other storage locations, which may be internal or external to OneLake. What are two places you can create shortcuts in fabric? (Select 2)
정답：A,B
설명：
Option A is correct because fabric allows you to create shortcuts within Lakehouses. This functionality is particularly useful as it provides a streamlined way to organize and manage data within a Lakehouse environment. By using these shortcuts, you can efficiently point to other storage locations, thereby enhancing accessibility and reducing the complexity of data retrieval.
Option B is correct because when you create a KQL database, a Shortcuts folder is automatically present. This folder is integral to the organization of the database, allowing users to create shortcuts that facilitate easy access to various data points and resources within the database.
Reference:
https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts
질문 # 122
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
정답：
설명：
Explanation:
To identify the slowest DAX query and analyze the time it spends in the formula engine compared to the storage engine, you should perform the following actions in sequence:
* From Performance analyzer, capture a recording.
* View the Server Timings tab.
* Enable Query Timings and Server Timings. Run the query.
* View the Query Timings tab.
* Sort the Duration (ms) column in descending order by DAX query time.
질문 # 123
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