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  1. Home
  2. Microsoft Certification
  3. DP-203 Exam
  4. Microsoft.DP-203.v2024-01-19.q230 Dumps
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Question 41

You are developing an Azure Synapse Analytics pipeline that will include a mapping data flow named Dataflow1. Dataflow1 will read customer data from an external source and use a Type 1 slowly changing dimension (SCO) when loading the data into a table named DimCustomer1 in an Azure Synapse Analytics dedicated SQL pool.
You need to ensure that Dataflow1 can perform the following tasks:
* Detect whether the data of a given customer has changed in the DimCustomer table.
* Perform an upsert to the DimCustomer table.
Which type of transformation should you use for each task? To answer, select the appropriate options in the answer area NOTE; Each correct selection is worth one point.

Correct Answer:

Explanation
insert code

Question 42

You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.
The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern.
/{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.json You need to prepare the data for batch data processing so that there is one dataset per hour per deviceType. The solution must minimize read times.
How should you configure the sink for the copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers
https://docs.microsoft.com/en-us/azure/data-factory/connector-file-system
insert code

Question 43

You are creating an Apache Spark job in Azure Databricks that will ingest JSON-formatted data.
You need to convert a nested JSON string into a DataFrame that will contain multiple rows.
Which Spark SQL function should you use?

Correct Answer: A
Convert nested JSON to a flattened DataFrame
You can to flatten nested JSON, using only $"column.*" and explode methods.
Note: Extract and flatten
Use $"column.*" and explode methods to flatten the struct and array types before displaying the flattened DataFrame.
Scala
display(DF.select($"id" as "main_id",$"name",$"batters",$"ppu",explode($"topping")) // Exploding the topping column using explode as it is an array type
.withColumn("topping_id",$"col.id") // Extracting topping_id from col using DOT form
.withColumn("topping_type",$"col.type") // Extracting topping_tytpe from col using DOT form
.drop($"col")
.select($"*",$"batters.*") // Flattened the struct type batters tto array type which is batter
.drop($"batters")
.select($"*",explode($"batter"))
.drop($"batter")
.withColumn("batter_id",$"col.id") // Extracting batter_id from col using DOT form
.withColumn("battter_type",$"col.type") // Extracting battter_type from col using DOT form
.drop($"col")
)
insert code

Question 44

You are developing an application that uses Azure Data Lake Storage Gen 2.
You need to recommend a solution to grant permissions to a specific application for a limited time period.
What should you include in the recommendation?

Correct Answer: B
A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:
What resources the client may access.
What permissions they have to those resources.
How long the SAS is valid.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview
insert code

Question 45

You have an Azure Data Factory pipeline that performs an incremental load of source data to an Azure Data Lake Storage Gen2 account.
Data to be loaded is identified by a column named LastUpdatedDate in the source table.
You plan to execute the pipeline every four hours.
You need to ensure that the pipeline execution meets the following requirements:
Automatically retries the execution when the pipeline run fails due to concurrency or throttling limits.
Supports backfilling existing data in the table.
Which type of trigger should you use?

Correct Answer: D
In case of pipeline failures, tumbling window trigger can retry the execution of the referenced pipeline automatically, using the same input parameters, without the user intervention. This can be specified using the property "retryPolicy" in the trigger definition.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger
insert code
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