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DP-203 Practice Questions

Question # 1

You create an Azure Databricks cluster and specify an additional library to install.
When you attempt to load the library to a notebook, the library in not found.
You need to identify the cause of the issue.
What should you review?

A.

notebook logs

B.

cluster event logs

C.

global init scripts logs

D.

workspace logs



C.

global init scripts logs


Explanation:
Cluster-scoped Init Scripts: Init scripts are shell scripts that run during the startup of each
cluster node before the Spark driver or worker JVM starts. Databricks customers use init
scripts for various purposes such as installing custom libraries, launching background
processes, or applying enterprise security policies.
Logs for Cluster-scoped init scripts are now more consistent with Cluster Log Delivery and
can be found in the same root folder as driver and executor logs for the cluster



Question # 2

You manage an enterprise data warehouse in Azure Synapse Analytics.
Users report slow performance when they run commonly used queries. Users do not report
performance changes for infrequently used queries.
You need to monitor resource utilization to determine the source of the performance
issues. Which metric should you monitor?

A.

Data IO percentage

B.

Local tempdb percentage

C.

Cache used percentage

D.

DWU percentage



C.

Cache used percentage




Question # 3

You have an Azure Databricks resource.
You need to log actions that relate to changes in compute for the Databricks resource.
Which Databricks services should you log?

A.

clusters

B.

workspace

C.

DBFS

D.

SSH

E.

lobs



B.

workspace




Question # 4

You are designing the folder structure for an Azure Data Lake Storage Gen2 account.
You identify the following usage patterns:
• Users will query data by using Azure Synapse Analytics serverless SQL pools and Azure
Synapse Analytics serverless Apache Spark pods.
• Most queries will include a filter on the current year or week.
• Data will be secured by data source.
You need to recommend a folder structure that meets the following requirements:
• Supports the usage patterns
• Simplifies folder security
• Minimizes query times
Which folder structure should you recommend?

A.

Option A

B.

Option B

C.

Option C

D.

Option D

E.

Option E



D.

Option D




Question # 5

You have an Azure Data lake Storage account that contains a staging zone.
You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a
data warehouse in Azure Synapse Analytics.
Solution You use an Azure Data Factory schedule trigger to execute a pipeline that
executes an Azure Databricks notebook, and then inserts the data into the data warehouse
Dow this meet the goal?

A.

Yes

B.

No



A.

Yes




Question # 6

You have an Azure Synapse Analytics dedicated SQL pool named SA1 that contains a table named Table1. You need to identify tables that have a high percentage of deleted rows. What should you run?

A.

Option

B.

Option

C.

Option

D.

Option



B.

Option




Question # 7

You are monitoring an Azure Stream Analytics job by using metrics in Azure.
You discover that during the last 12 hours, the average watermark delay is consistently greater than the configured late arrival tolerance.What is a possible cause of this behavior?

A.

Events whose application timestamp is earlier than their arrival time by more than five minutes arrive as inputs.

B.

There are errors in the input data.

C.

The late arrival policy causes events to be dropped.

D.

The job lacks the resources to process the volume of incoming data



D.

The job lacks the resources to process the volume of incoming data


Explanation:
Watermark Delay indicates the delay of the streaming data processing job.
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. To scale up resources, see Understand and adjust Streaming Units.
Not enough throughput within the input event brokers, so they are throttled. For
possible solutions, see Automatically scale up Azure Event Hubs throughput units.
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.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-time-handling



Question # 8

You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.
Which Azure Storage functionality should you include in the solution?

A.

change feed

B.

soft delete

C.

time-based retention

D.

lifecycle management



D.

lifecycle management


Explanation:
Scenario: Purge Twitter feed data records that are older than two years.
Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But
the need for access often drops drastically as the data ages. Some data remains idle in the
cloud and is rarely accessed once stored. Some data sets expire days or months after
creation, while other data sets are actively read and modified throughout their lifetimes.
Azure Storage lifecycle management offers a rule-based policy that you can use to
transition blob data to the appropriate access tiers or to expire data at the end of the data
lifecycle.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview



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Microsoft DP-203 Exam Dumps

Exam Name: Data Engineering on Microsoft Azure
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  • Total Questions: 341
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