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The Microsoft DP-203 exam is intended for data engineers who work with Azure services such as Azure Data Factory, Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics. DP-203 exam is also suitable for professionals who want to learn how to use Azure services for data engineering tasks. DP-203 Exam is conducted online and is available in multiple languages, making it accessible to professionals worldwide.

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q293-Q298):

NEW QUESTION # 293
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.

All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 294
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.

Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Explanation

Box 1: Hash-distributed
Box 2: ProductKey
ProductKey is used extensively in joins.
Hash-distributed tables improve query performance on large fact tables.
Box 3: Round-robin
Box 4: RegionKey
Round-robin tables are useful for improving loading speed.
Consider using the round-robin distribution for your table in the following scenarios:
* When getting started as a simple starting point since it is the default
* If there is no obvious joining key
* If there is not good candidate column for hash distributing the table
* If the table does not share a common join key with other tables
* If the join is less significant than other joins in the query
* When the table is a temporary staging table
Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

NEW QUESTION # 295
You have an Azure Synapse Analytics dedicated SQL pool.
You need to monitor the database for long-running queries and identify which queries are waiting on resources Which dynamic manage ment view should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE; Each correct answer is worth one point.

Answer:

Explanation:

NEW QUESTION # 296
You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
* Status: Running
* Type: Self-Hosted
* Version: 4.4.7292.1
* Running / Registered Node(s): 1/1
* High Availability Enabled: False
* Linked Count: 0
* Queue Length: 0
* Average Queue Duration. 0.00s
The integration runtime has the following node details:
* Name: X-M
* Status: Running
* Version: 4.4.7292.1
* Available Memory: 7697MB
* CPU Utilization: 6%
* Network (In/Out): 1.21KBps/0.83KBps
* Concurrent Jobs (Running/Limit): 2/14
* Role: Dispatcher/Worker
* Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Box 1: fail until the node comes back online
We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.
Box 2: lowered
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%
Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

NEW QUESTION # 297
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
A workload for data engineers who will use Python and SQL.
A workload for jobs that will run notebooks that use Python, Scala, and SOL.
A workload that data scientists will use to perform ad hoc analysis in Scala and R.
The enterprise architecture team at your company identifies the following standards for Databricks environments:
The data engineers must share a cluster.
The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
You need to create the Databricks clusters for the workloads.
Solution: You create a Standard cluster for each data scientist, a Standard cluster for the data engineers, and a High Concurrency cluster for the jobs.
Does this meet the goal?

A. Yes
B. No

Answer: B

Explanation:
We need a High Concurrency cluster for the data engineers and the jobs.
Note: Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.
Reference:
https://docs.azuredatabricks.net/clusters/configure.html

NEW QUESTION # 298
......

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Mark Lee Mark Lee

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