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Microsoft DP-203 Exam

Certification Provider: Microsoft
Exam Name: Data Engineering on Microsoft Azure
Number of questions in our database: 237
Exam Version: Jan. 24, 2022
DP-203 Exam Official Topics:
  • Topic 1: Monitor and Optimize Data Storage and Data Processing/ Implement physical data storage structures
  • Topic 2: Design metastores in Azure Synapse Analytics and Azure Databricks/ Transform data by using Azure Synapse Pipelines
  • Topic 3: Implement different table geometries with Azure Synapse Analytics pools/ Design data encryption for data at rest and in transit
  • Topic 4: Identify when partitioning is needed in Azure Data Lake Storage Gen2/ Design and develop slowly changing dimensions
  • Topic 5: Design a folder structure that represents the levels of data transformation/ Optimize and troubleshoot data storage and data processing
  • Topic 6: Implement file and folder structures for efficient querying and data pruning/ Design a data storage structure
  • Topic 7: Deliver data in a relational star schema/ Design slowly changing dimensions
  • Topic 8: Configure error handling for the transformation/ Design and Develop Data Processing
  • Topic 9: Design and develop a batch processing solution/ Implement logical data structures
  • Topic 10: Optimize pipelines for analytical or transactional purposes/ Transform data by using Stream Analytics
  • Topic 11: Design and develop a stream processing solution/ Implement a dimensional hierarchy

Free Microsoft DP-203 Exam Actual Questions

The questions for DP-203 were last updated On Jan. 24, 2022

Question #1

You are designing an anomaly detection solution for streaming data from an Azure IoT hub. The solution must meet the following requirements:

Send the output to Azure Synapse.

Identify spikes and dips in time series data.

Minimize development and configuration effort.

Which should you include in the solution?

Reveal Solution Hide Solution
Correct Answer: B

You can identify anomalies by routing data via IoT Hub to a built-in ML model in Azure Stream Analytics.


https://docs.microsoft.com/en-us/learn/modules/data-anomaly-detection-using-azure-iot-hub/

Question #2

A company uses Azure Stream Analytics to monitor devices.

The company plans to double the number of devices that are monitored.

You need to monitor a Stream Analytics job to ensure that there are enough processing resources to handle the additional load.

Which metric should you monitor?

Reveal Solution Hide Solution
Correct Answer: C

There are a number of resource constraints that can cause the streaming pipeline to slow down. The watermark delay metric can rise due to:

Not enough processing resources in Stream Analytics to handle the volume of input events.

Not enough throughput within the input event brokers, so they are throttled.

Output sinks are not provisioned with enough capacity, so they are throttled. The possible solutions vary widely based on the flavor of output service being used.


https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling

Question #3

You have an activity in an Azure Data Factory pipeline. The activity calls a stored procedure in a data warehouse in Azure Synapse Analytics and runs daily.

You need to verify the duration of the activity when it ran last.

What should you use?

Reveal Solution Hide Solution
Question #4

You are designing database for an Azure Synapse Analytics dedicated SQL pool to support workloads for detecting ecommerce transaction fraud.

Data will be combined from multiple ecommerce sites and can include sensitive financial information such as credit card numbers.

You need to recommend a solution that meets the following requirements:

Users must be able to identify potentially fraudulent transactions.

Users must be able to use credit cards as a potential feature in models.

Users must NOT be able to access the actual credit card numbers.

What should you include in the recommendation?

Reveal Solution Hide Solution
Correct Answer: C

Use Always Encrypted to secure the required columns. You can configure Always Encrypted for individual database columns containing your sensitive data. Always Encrypted is a feature designed to protect sensitive data, such as credit card numbers or national identification numbers (for example, U.S. social security numbers), stored in Azure SQL Database or SQL Server databases.


https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/always-encrypted-database-engine

Question #5

You have an Azure subscription linked to an Azure Active Directory (Azure AD) tenant that contains a service principal named ServicePrincipal1. The subscription contains an Azure Data Lake Storage account named adls1. Adls1 contains a folder named Folder2 that has a URI of https://adls1.dfs.core.windows.net/container1/Folder1/Folder2/.

ServicePrincipal1 has the access control list (ACL) permissions shown in the following table.

You need to ensure that ServicePrincipal1 can perform the following actions:

Traverse child items that are created in Folder2.

Read files that are created in Folder2.

The solution must use the principle of least privilege.

Which two permissions should you grant to ServicePrincipal1 for Folder2? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Reveal Solution Hide Solution
Correct Answer: D, F

Execute (X) permission is required to traverse the child items of a folder.

There are two kinds of access control lists (ACLs), Access ACLs and Default ACLs.

Access ACLs: These control access to an object. Files and folders both have Access ACLs.

Default ACLs: A 'template' of ACLs associated with a folder that determine the Access ACLs for any child items that are created under that folder. Files do not have Default ACLs.


https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-access-control


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