A Review Of Vivid DP-203 Practice Test

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NEW QUESTION 1

You have an Azure Data Lake Storage account that has a virtual network service endpoint configured.
You plan to use Azure Data Factory to extract data from the Data Lake Storage account. The data will then be loaded to a data warehouse in Azure Synapse Analytics by using PolyBase.
Which authentication method should you use to access Data Lake Storage?

  • A. shared access key authentication
  • B. managed identity authentication
  • C. account key authentication
  • D. service principal authentication

Answer: B

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-sql-data-warehouse#use-polybase-to-load-d

NEW QUESTION 2

You are designing an Azure Databricks interactive cluster. The cluster will be used infrequently and will be configured for auto-termination.
You need to ensure that the cluster configuration is retained indefinitely after the cluster is terminated. The solution must minimize costs.
What should you do?

  • A. Clone the cluster after it is terminated.
  • B. Terminate the cluster manually when processing completes.
  • C. Create an Azure runbook that starts the cluster every 90 days.
  • D. Pin the cluster.

Answer: D

Explanation:
To keep an interactive cluster configuration even after it has been terminated for more than 30 days, an administrator can pin a cluster to the cluster list.
References:
https://docs.azuredatabricks.net/clusters/clusters-manage.html#automatic-termination

NEW QUESTION 3

You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Datiabricks and PolyBase in Azure Synapse Analytics.
You need to recommend a Stream Analytics data output format to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the tiles can be queried quickly and that the data type information is retained.
What should you recommend?

  • A. Parquet
  • B. Avro
  • C. CSV
  • D. JSON

Answer: B

Explanation:
The Avro format is great for data and message preservation.Avro schema with its support for evolution is essential for making the data robust for streaming architectures like Kafka, and with the metadata that schema provides, you can reason on the data. Having a schema provides robustness in providing meta-data about the data stored in Avro records which are self- documenting the data.References: http://cloudurable.com/blog/avro/index.html

NEW QUESTION 4

You have files and folders in Azure Data Lake Storage Gen2 for an Azure Synapse workspace as shown in the following exhibit.
DP-203 dumps exhibit
You create an external table named ExtTable that has LOCATION='/topfolder/'.
When you query ExtTable by using an Azure Synapse Analytics serverless SQL pool, which files are returned?

  • A. File2.csv and File3.csv only
  • B. File1.csv and File4.csv only
  • C. File1.csv, File2.csv, File3.csv, and File4.csv
  • D. File1.csv only

Answer: C

Explanation:
To run a T-SQL query over a set of files within a folder or set of folders while treating them as a single entity or rowset, provide a path to a folder or a pattern (using wildcards) over a set of files or folders. Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage#query-multiple-files-or-folders

NEW QUESTION 5

You are designing a statistical analysis solution that will use custom proprietary1 Python functions on near real-time data from Azure Event Hubs.
You need to recommend which Azure service to use to perform the statistical analysis. The solution must minimize latency.
What should you recommend?

  • A. Azure Stream Analytics
  • B. Azure SQL Database
  • C. Azure Databricks
  • D. Azure Synapse Analytics

Answer: A

NEW QUESTION 6

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.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
DP-203 dumps exhibit

NEW QUESTION 7

You have an Azure Synapse Analytics dedicated SQL pool that contains a large fact table. The table contains 50 columns and 5 billion rows and is a heap.
Most queries against the table aggregate values from approximately 100 million rows and return only two columns.
You discover that the queries against the fact table are very slow. Which type of index should you add to provide the fastest query times?

  • A. nonclustered columnstore
  • B. clustered columnstore
  • C. nonclustered
  • D. clustered

Answer: B

Explanation:
Clustered columnstore indexes are one of the most efficient ways you can store your data in dedicated SQL pool.
Columnstore tables won't benefit a query unless the table has more than 60 million rows. Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

NEW QUESTION 8

You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Step 1: Create an Azure Storage account that has a lifecycle policy
To automate common data management tasks, Microsoft created a solution based on Azure Data Factory. The service, Data Lifecycle Management, makes frequently accessed data available and archives or purges other data according to retention policies. Teams across the company use the service to reduce storage costs, improve app performance, and comply with data retention policies.
Step 2: Create a Log Analytics workspace that has Data Retention set to 120 days.
Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis to multiple different targets, such as a Storage Account: Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
Step 3: From Azure Portal, add a diagnostic setting. Step 4: Send the data to a log Analytics workspace,
Event Hub: A pipeline that transfers events from services to Azure Data Explorer. Keeping Azure Data Factory metrics and pipeline-run data.
Configure diagnostic settings and workspace.
Create or add diagnostic settings for your data factory.
DP-203 dumps exhibit In the portal, go to Monitor. Select Settings > Diagnostic settings.
DP-203 dumps exhibit Select the data factory for which you want to set a diagnostic setting.
DP-203 dumps exhibit If no settings exist on the selected data factory, you're prompted to create a setting. Select Turn on diagnostics.
DP-203 dumps exhibit Give your setting a name, select Send to Log Analytics, and then select a workspace from Log Analytics Workspace.
DP-203 dumps exhibit Select Save. Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

NEW QUESTION 9

You are creating dimensions for a data warehouse in an Azure Synapse Analytics dedicated SQL pool. You create a table by using the Transact-SQL statement shown in the following exhibit.
DP-203 dumps exhibit
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Type 2
A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.
Reference:
https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapse-analytics

NEW QUESTION 10

You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.
DP-203 dumps 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 area.
NOTE: Each correct selection is worth one point.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
DP-203 dumps exhibit

NEW QUESTION 11

You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
DP-203 dumps exhibit Status: Running
DP-203 dumps exhibit Type: Self-Hosted
DP-203 dumps exhibit Version: 4.4.7292.1
DP-203 dumps exhibit Running / Registered Node(s): 1/1
DP-203 dumps exhibit High Availability Enabled: False
DP-203 dumps exhibit Linked Count: 0
DP-203 dumps exhibit Queue Length: 0
DP-203 dumps exhibit Average Queue Duration. 0.00s
The integration runtime has the following node details:
DP-203 dumps exhibit Name: X-M
DP-203 dumps exhibit Status: Running
DP-203 dumps exhibit Version: 4.4.7292.1
DP-203 dumps exhibit Available Memory: 7697MB
DP-203 dumps exhibit CPU Utilization: 6%
DP-203 dumps exhibit Network (In/Out): 1.21KBps/0.83KBps
DP-203 dumps exhibit Concurrent Jobs (Running/Limit): 2/14
DP-203 dumps exhibit Role: Dispatcher/Worker
DP-203 dumps exhibit 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.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

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 12

You need to integrate the on-premises data sources and Azure Synapse Analytics. The solution must meet the data integration requirements.
Which type of integration runtime should you use?

  • A. Azure-SSIS integration runtime
  • B. self-hosted integration runtime
  • C. Azure integration runtime

Answer: C

NEW QUESTION 13

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 are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a session window that uses a timeout size of 10 seconds. Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Instead use a tumbling window. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous
time intervals. Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

NEW QUESTION 14

You plan to implement an Azure Data Lake Gen2 storage account.
You need to ensure that the data lake will remain available if a data center fails in the primary Azure region. The solution must minimize costs.
Which type of replication should you use for the storage account?

  • A. geo-redundant storage (GRS)
  • B. zone-redundant storage (ZRS)
  • C. locally-redundant storage (LRS)
  • D. geo-zone-redundant storage (GZRS)

Answer: A

Explanation:
Geo-redundant storage (GRS) copies your data synchronously three times within a single physical location in the primary region using LRS. It then copies your data asynchronously to a single physical location in the secondary region.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy

NEW QUESTION 15

You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.
How should you complete the Data Factory data flow script? 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.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.
Box 1: dept=='ecommerce', dept=='retail', dept=='wholesale'
First we put the condition. The order must match the stream labeling we define in Box 3. Syntax:
<incomingStream> split(
<conditionalExpression1>
<conditionalExpression2>
disjoint: {true | false}
) ~> <splitTx>@(stream1, stream2, ..., <defaultStream>)
Box 2: discount : false
disjoint is false because the data goes to the first matching condition. All remaining rows matching the third condition go to output stream all.
Box 3: ecommerce, retail, wholesale, all Label the streams
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-conditional-split

NEW QUESTION 16

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:
DP-203 dumps exhibit A workload for data engineers who will use Python and SQL.
DP-203 dumps exhibit A workload for jobs that will run notebooks that use Python, Scala, and SOL.
DP-203 dumps exhibit 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:
DP-203 dumps exhibit The data engineers must share a cluster.
DP-203 dumps exhibit 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.
DP-203 dumps exhibit 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 High Concurrency cluster for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
We would need a High Concurrency cluster for 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 17

You are designing a partition strategy for a fact table in an Azure Synapse Analytics dedicated SQL pool. The table has the following specifications:
• Contain sales data for 20,000 products.
• Use hash distribution on a column named ProduclID,
• Contain 2.4 billion records for the years 20l9 and 2020.
Which number of partition ranges provides optimal compression and performance of the clustered columnstore index?

  • A. 40
  • B. 240
  • C. 400
  • D. 2,400

Answer: B

NEW QUESTION 18

You build an Azure Data Factory pipeline to move data from an Azure Data Lake Storage Gen2 container to a database in an Azure Synapse Analytics dedicated SQL pool.
Data in the container is stored in the following folder structure.
/in/{YYYY}/{MM}/{DD}/{HH}/{mm}
The earliest folder is /in/2021/01/01/00/00. The latest folder is /in/2021/01/15/01/45. You need to configure a pipeline trigger to meet the following requirements:
DP-203 dumps exhibit Existing data must be loaded.
DP-203 dumps exhibit Data must be loaded every 30 minutes.
DP-203 dumps exhibit Late-arriving data of up to two minutes must he included in the load for the time at which the data should have arrived.
How should you configure the pipeline trigger? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
DP-203 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Tumbling window
To be able to use the Delay parameter we select Tumbling window. Box 2:
Recurrence: 30 minutes, not 32 minutes
Delay: 2 minutes.
The amount of time to delay the start of data processing for the window. The pipeline run is started after the expected execution time plus the amount of delay. The delay defines how long the trigger waits past the due time before triggering a new run. The delay doesn’t alter the window startTime.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger

NEW QUESTION 19
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