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Updated DP-203 Dumps Questions For Microsoft Exam
Microsoft DP-203 certification exam is intended for data engineers who work with data scientists, data analysts, and business stakeholders to implement data solutions on the Microsoft Azure platform. DP-203 exam covers various topics such as Azure data storage, Azure data processing, Azure data integration, and data transformation. It also tests your knowledge of key Azure services such as Azure Data Factory, Azure Synapse Analytics, and Azure Stream Analytics.
Why do you need to get Microsoft DP-203 Exam
Are you ready to take the Data Engineering on Microsoft Azure Exam
DP-203 Exam: Key Points and Focus Areas
The Microsoft Data Platform, or MDP, is a collection of products and services that primarily target companies whose business involves large volumes of data. These companies are often referred to as data-driven businesses. The MDP helps such businesses store, process, and analyze the massive amounts of data that their operations produce. The Microsoft Data Platform Certification (Microsoft DP-203) exam tests a candidate's knowledge of the Microsoft technologies that are used to build a cloud computing infrastructure for Big Data analytics. Free demo material that helps with the preparation for the DP-203 exam includes sample questions and answers, practice exams, and a training guide. Microsoft DP-203 Dumps provide all the test-taking tools necessary for passing the DP-203 exam, including a test engine and a simulation mode. If you are reading this article it means you are interested in pursuing a Microsoft certification path or already have one. Great! You've taken an important first step to become a Data Engineer on Microsoft Azure certification.
NEW QUESTION # 14
You have an Apache Spark DataFrame named temperatures. A sample of the data is shown in the following table.
You need to produce the following table by using a Spark SQL query.
How should you complete the query? 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
Text Description automatically generated
Box 1: PIVOT
PIVOT rotates a table-valued expression by turning the unique values from one column in the expression into multiple columns in the output. And PIVOT runs aggregations where they're required on any remaining column values that are wanted in the final output.
Reference:
https://learnsql.com/cookbook/how-to-convert-an-integer-to-a-decimal-in-sql-server/
https://docs.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot
NEW QUESTION # 15
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool. You plan to deploy a solution that will analyze sales data and include the following:
* A table named Country that will contain 195 rows
* A table named Sales that will contain 100 million rows
* A query to identify total sales by country and customer from the past 30 days You need to create the tables. The solution must maximize query performance.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 16
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool. You plan to deploy a solution that will analyze sales data and include the following:
* A table named Country that will contain 195 rows
* A table named Sales that will contain 100 million rows
* A query to identify total sales by country and customer from the past 30 days You need to create the tables. The solution must maximize query performance.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 17
The following code segment is used to create an Azure Databricks cluster.
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
Graphical user interface, text, application Description automatically generated
Box 1: Yes
A cluster mode of 'High Concurrency' is selected, unlike all the others which are 'Standard'. This results in a worker type of Standard_DS13_v2.
Box 2: No
When you run a job on a new cluster, the job is treated as a data engineering (job) workload subject to the job workload pricing. When you run a job on an existing cluster, the job is treated as a data analytics (all-purpose) workload subject to all-purpose workload pricing.
Box 3: Yes
Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns.
Reference:
https://adatis.co.uk/databricks-cluster-sizing/
https://docs.microsoft.com/en-us/azure/databricks/jobs
https://docs.databricks.com/administration-guide/capacity-planning/cmbp.html
https://docs.databricks.com/delta/index.html
NEW QUESTION # 18
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?
- A. coalesce
- B. extract
- C. explode
- D. filter
Answer: C
Explanation:
Explanation
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") ) Reference: https://learn.microsoft.com/en-us/azure/databricks/kb/scala/flatten-nested-columns-dynamically
NEW QUESTION # 19
You are designing an Azure Data Lake Storage Gen2 container to store data for the human resources (HR) department and the operations department at your company. You have the following data access requirements:
* After initial processing, the HR department data will be retained for seven years.
* The operations department data will be accessed frequently for the first six months, and then accessed once per month.
You need to design a data retention solution to meet the access requirements. The solution must minimize storage costs.
Answer:
Explanation:
NEW QUESTION # 20
You have an Azure Data Lake Storage Gen 2 account named storage1.
You need to recommend a solution for accessing the content in storage1. The solution must meet the following requirements:
* List and read permissions must be granted at the storage account level.
* Additional permissions can be applied to individual objects in storage1.
* Security principals from Microsoft Azure Active Directory (Azure AD), part of Microsoft Entra, must be used for authentication.
What should you use? To answer, drag the appropriate components to the correct requirements. Each component 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
Box 1: Role-based access control (RBAC) roles
List and read permissions must be granted at the storage account level.
Security principals from Microsoft Azure Active Directory (Azure AD), part of Microsoft Entra, must be used for authentication.
Role-based access control (Azure RBAC)
Azure RBAC uses role assignments to apply sets of permissions to security principals. A security principal is an object that represents a user, group, service principal, or managed identity that is defined in Azure Active Directory (AD). A permission set can give a security principal a "coarse-grain" level of access such as read or write access to all of the data in a storage account or all of the data in a container.
Box 2: Access control lists (ACLs)
Additional permissions can be applied to individual objects in storage1.
Access control lists (ACLs)
ACLs give you the ability to apply "finer grain" level of access to directories and files. An ACL is a permission construct that contains a series of ACL entries. Each ACL entry associates security principal with an access level.
Reference: https://learn.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control-model
NEW QUESTION # 21
You have an Azure Synapse Analytics dedicated SQL pool named SQL1 that contains a hash-distributed fact table named Table1.
You need to recreate Table1 and add a new distribution column. The solution must maximize the availability of data.
Which four 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.
Answer:
Explanation:
1 - Drop the indexes of Table1.
2 - Create a new table named Table 1v2 by running CTAS
3 - Rename Table1 as Table1_old.
4 - Rename Table 1v2 as Table1.
NEW QUESTION # 22
You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.
You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.
You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.
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:
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION # 23
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?
- A. tumbling
- B. hopping
- C. snapshot
- D. sliding
Answer: A
Explanation:
Explanation
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
NEW QUESTION # 24
You have an Azure SQL database named Database1 and two Azure event hubs named HubA and HubB. The data consumed from each source is shown in the following table.
You need to implement Azure Stream Analytics to calculate the average fare per mile by driver.
How should you configure the Stream Analytics input for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
HubA: Stream
HubB: Stream
Database1: Reference
Reference data (also known as a lookup table) is a finite data set that is static or slowly changing in nature, used to perform a lookup or to augment your data streams. For example, in an IoT scenario, you could store metadata about sensors (which don't change often) in reference data and join it with real time IoT data streams. Azure Stream Analytics loads reference data in memory to achieve low latency stream processing Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
NEW QUESTION # 25
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of
{YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Table Description automatically generated
Box 1: Incremental load
Box 2: Tumbling window
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.
Timeline Description automatically generated
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
NEW QUESTION # 26
You are designing an Azure Stream Analytics solution that receives instant messaging data from an Azure Event Hub.
You need to ensure that the output from the Stream Analytics job counts the number of messages per time zone every 15 seconds.
How should you complete the Stream Analytics query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Table Description automatically generated
Box 1: timestamp by
Box 2: TUMBLINGWINDOW
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
Timeline Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
NEW QUESTION # 27
You have an Azure Data Factory instance that contains two pipelines named Pipeline1 and Pipeline2.
Pipeline1 has the activities shown in the following exhibit.
Pipeline2 has the activities shown in the following exhibit.
You execute Pipeline2, and Stored procedure1 in Pipeline1 fails.
What is the status of the pipeline runs?
- A. Pipeline1 and Pipeline2 failed.
- B. Pipeline1 succeeded and Pipeline2 failed.
- C. Pipeline1 failed and Pipeline2 succeeded.
- D. Pipeline1 and Pipeline2 succeeded.
Answer: D
Explanation:
Explanation
Activities are linked together via dependencies. A dependency has a condition of one of the following:
Succeeded, Failed, Skipped, or Completed.
Consider Pipeline1:
If we have a pipeline with two activities where Activity2 has a failure dependency on Activity1, the pipeline will not fail just because Activity1 failed. If Activity1 fails and Activity2 succeeds, the pipeline will succeed.
This scenario is treated as a try-catch block by Data Factory.
Waterfall chart Description automatically generated with medium confidence
The failure dependency means this pipeline reports success.
Note:
If we have a pipeline containing Activity1 and Activity2, and Activity2 has a success dependency on Activity1, it will only execute if Activity1 is successful. In this scenario, if Activity1 fails, the pipeline will fail.
Reference:
https://datasavvy.me/category/azure-data-factory/
NEW QUESTION # 28
You have an Azure Data Lake Storage Gen2 account named account1 that stores logs as shown in the following table.
You do not expect that the logs will be accessed during the retention periods.
You need to recommend a solution for account1 that meets the following requirements:
* Automatically deletes the logs at the end of each retention period
* Minimizes storage costs
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Store the infrastructure logs in the Cool access tier and the application logs in the Archive access tier For infrastructure logs: Cool tier - An online tier optimized for storing data that is infrequently accessed or modified. Data in the cool tier should be stored for a minimum of 30 days. The cool tier has lower storage costs and higher access costs compared to the hot tier.
For application logs: Archive tier - An offline tier optimized for storing data that is rarely accessed, and that has flexible latency requirements, on the order of hours. Data in the archive tier should be stored for a minimum of 180 days.
Box 2: Azure Blob storage lifecycle management rules
Blob storage lifecycle management offers a rule-based policy that you can use to transition your data to the desired access tier when your specified conditions are met. You can also use lifecycle management to expire data at the end of its life.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview
NEW QUESTION # 29
You need to build a solution to ensure that users can query specific files in an Azure Data Lake Storage Gen2 account from an Azure Synapse Analytics serverless SQL pool.
Which three 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
Step 1: Create an external data source
You can create external tables in Synapse SQL pools via the following steps:
* CREATE EXTERNAL DATA SOURCE to reference an external Azure storage and specify the credential that should be used to access the storage.
* CREATE EXTERNAL FILE FORMAT to describe format of CSV or Parquet files.
* CREATE EXTERNAL TABLE on top of the files placed on the data source with the same file format.
Step 2: Create an external file format object
Creating an external file format is a prerequisite for creating an external table.
Step 3: Create an external table
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
NEW QUESTION # 30
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