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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain a data solution | 10-15% | - Deploy data assets
|
| Topic 2: Load and prepare data | 20-25% | - Ingest data from source systems
|
| Topic 3: Design and manage the data model | 20-25% | - Implement and configure a data model
|
| Topic 4: Secure and monitor data solutions | 15-20% | - Monitor data solutions
|
| Topic 5: Clean, transform, and enrich data | 25-30% | - Clean data
|
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NEW QUESTION # 157
Hotspot Question
You have a Fabric tenant that contains a semantic model named Model1.
Model1 contains the following tables.
Product and Store can be part of multiple campaigns, and multiple active campaigns can exist simultaneously.
You plan to create a report named Report1 that will display sales made after a campaign for a given store.
You need to add a table to Model1 and configure cardinality for the table's relationships.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 158
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:
NEW QUESTION # 159
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.
Answer:
Explanation:
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 " )
NEW QUESTION # 160
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.
You create a Fabric notebook and attach it to lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
* Add a column named pickupDate that will contain only the date portion of pickupDateTime.
* Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Add the pickupDate column: .withColumn( " pickupDate " , df[ " pickupDateTime " ].cast( " date " )) Filter the DataFrame: .filter( " fareAmount > 0 AND fareAmount < 100 " ) In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast( " date " ) function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than 0 and less than 100, thus ensuring only positive values less than 100 are included.
NEW QUESTION # 161
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.
Which three Data view options should you select? Each correct answer presents part of the solution. NOTE:
Each correct answer is worth one point.
Answer: A,B,C
Explanation:
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
References: These features and their descriptions are typically found in the Power Query documentation, under the section for data profiling and quality features.
NEW QUESTION # 162
......
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