Latest PDF of DP-203: Data Engineering on Microsoft Azure

Data Engineering on Microsoft Azure Practice Test

DP-203 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

Test Detail:
The DP-203 exam, Data Engineering on Microsoft Azure, is designed to validate the skills and knowledge of data engineers working with Azure technologies for data storage, processing, and analytics. The exam assesses candidates' abilities to design and implement data solutions using various Azure services and tools.

Course Outline:
The course for DP-203 certification covers a wide range of syllabus related to data engineering on Microsoft Azure. The following is a general outline of the key areas covered:

1. Introduction to Data Engineering on Azure:
- Understanding the role of a data engineer in Azure environments.
- Overview of Azure data services and their capabilities.
- Familiarization with data engineering concepts and best practices.

2. Data Storage and Processing:
- Azure data storage options, including Azure Storage, Azure Data Lake Storage, and Azure SQL Database.
- Implementing data ingestion and transformation using Azure Data Factory.
- Introduction to big data processing with Azure Databricks and HDInsight.

3. Data Orchestration and Integration:
- Implementing data orchestration workflows with Azure Logic Apps.
- Integration of data from various sources using Azure Synapse Pipelines.
- Familiarization with Azure Event Grid and Azure Service Bus for event-driven data processing.

4. Data Governance and Security:
- Implementing data security and compliance measures in Azure.
- Configuring access controls and encryption for data at rest and in transit.
- Understanding data privacy, governance, and auditing in Azure.

5. Data Analytics and Visualization:
- Introduction to Azure Synapse Analytics for data warehousing and analytics.
- Implementing data analytics solutions using Azure Analysis Services and Azure Power BI.
- Familiarization with Azure Machine Learning for predictive analytics and machine learning models.

Exam Objectives:
The DP-203 exam evaluates the candidate's knowledge and skills in the following key areas:

1. Designing and implementing data storage solutions on Azure.
2. Implementing data integration and orchestration workflows.
3. Configuring and managing data security and compliance measures.
4. Implementing data processing and analytics solutions.
5. Monitoring, troubleshooting, and optimizing data solutions on Azure.

Exam Syllabus:
The exam syllabus for DP-203 provides a detailed breakdown of the syllabus covered in each exam objective. It includes specific tasks, tools, and concepts that candidates should be proficient in. The syllabus may cover the following areas:

- Designing and implementing Azure data storage solutions
- Data ingestion, transformation, and orchestration using Azure Data Factory
- Data security, privacy, and compliance measures on Azure
- Configuring and optimizing data processing workflows
- Implementing data analytics and visualization solutions

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Microsoft
DP-203
Data Engineering on Microsoft Azure
https://killexams.com/pass4sure/exam-detail/DP-203
Question: 92
HOTSPOT
You need to design an analytical storage solution for the transactional data. The solution must meet the sales transaction dataset requirements.
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:
Graphical user
interface, text, application, table Description automatically generated Box 1: Round-robin
Round-robin tables are useful for improving loading speed.
Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month.
Box 2: Hash
Hash-distributed tables Boost query performance on large fact tables.
Question: 93
You have an Azure data factory.
You need to examine the pipeline failures from the last 180 flays. What should you use?
1. the Activity tog blade for the Data Factory resource
2. Azure Data Factory activity runs in Azure Monitor
3. Pipeline runs in the Azure Data Factory user experience
4. the Resource health blade for the Data Factory resource
Answer: B Explanation:
Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time.
Reference: https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor
Question: 94
HOTSPOT
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: Existing data must be loaded.
Data must be loaded every 30 minutes.
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.
Answer:
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.
Question: 95
HOTSPOT
You need to design a data ingestion and storage solution for the Twitter feeds. The solution must meet the customer sentiment analytics requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection b worth one point.
Answer:
Explanation:
Graphical user interface, text Description automatically generated
Box 1: Configure Evegent Hubs partitions
Scenario: Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage without purchasing additional throughput or capacity units.
Event Hubs is designed to help with processing of large volumes of events. Event Hubs throughput is scaled by using partitions and throughput-unit allocations.
Event Hubs traffic is controlled by TUs (standard tier). Auto-inflate enables you to start small with the minimum required TUs you choose. The feature then scales automatically to the maximum limit of TUs you need, depending on the increase in your traffic.
Box 2: An Azure Data Lake Storage Gen2 account
Scenario: Ensure that the data store supports Azure AD-based access control down to the object level.
Azure Data Lake Storage Gen2 implements an access control model that supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs).
Question: 96
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency. You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
1. Add a pass-through query.
2. Add a temporal analytic function.
3. Scale out the query by using PARTITION BY.
4. Convert the query to a reference query.
5. Increase the number of streaming units.
Answer: C,E Explanation:
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics
job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption Question: 97
HOTSPOT
You have an Azure subscription.
You need to deploy an Azure Data Lake Storage Gen2 Premium account. The solution must meet the following requirements:
* Blobs that are older than 365 days must be deleted.
* Administrator efforts must be minimized.
* Costs must be minimized
What should you use? To answer, select the appropriate options in the answer area. NOTE Each correct selection is worth one point.
Answer:
Explanation:
https://learn.microsoft.com/en-us/azure/storage/blobs/premium-tier-for-data-lake-storage
Question: 98
DRAG DROP
You need to ensure that the Twitter feed data can be analyzed in the dedicated SQL pool.
The solution must meet the customer sentiment analytics requirements.
Which three Transaction-SQL DDL commands should you run in sequence? To answer, move the appropriate commands from the list of commands 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:
Scenario: Allow Contoso users to use PolyBase in an Azure Synapse Analytics dedicated SQL pool to query the content of the data records that host the Twitter feeds. Data must be protected by using row-level security (RLS). The users must be authenticated by using their own Azure AD credentials.
Box 1: CREATE EXTERNAL DATA SOURCE
External data sources are used to connect to storage accounts. Box 2: CREATE EXTERNAL FILE FORMAT
CREATE EXTERNAL FILE FORMAT creates an external file format object that defines external data stored in Azure Blob Storage or Azure Data Lake Storage. Creating an external file format is a prerequisite for creating an external table.
Box 3: CREATE EXTERNAL TABLE AS SELECT
When used in conjunction with the CREATE TABLE AS SELECT statement, selecting from an external table imports data into a table within the SQL pool. In addition to the COPY statement, external tables are useful for loading data.
Question: 99
DRAG DROP
You have the following table named Employees.
You need to calculate the employee_type value based on the hire_date value.
How should you complete the Transact-SQL statement? 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: Graphical user
interface, text, application Description automatically generated Box 1: CASE
CASE evaluates a list of conditions and returns one of multiple possible result expressions.
CASE can be used in any statement or clause that allows a valid expression. For example, you can use CASE in statements such as SELECT, UPDATE, DELETE and SET, and in clauses such as select_list, IN, WHERE, ORDER BY, and HAVING.
Syntax: Simple CASE expression: CASE input_expression
WHEN when_expression THEN result_expression [ �n ] [ ELSE else_result_expression ] END Box 2: ELSE
Question: 100
HOTSPOT
You are building a database in an Azure Synapse Analytics serverless SQL pool. You have data stored in Parquet files in an Azure Data Lake Storage Gen2 container. Records are structured as shown in the following sample.
{
"id": 123,
"address_housenumber": "19c", "address_line": "Memory Lane", "applicant1_name": "Jane", "applicant2_name": "Dev"
}
The records contain two applicants at most.
You need to build a table that includes only the address fields.
How should you complete the Transact-SQL statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Box 1: CREATE EXTERNAL TABLE
An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.
Syntax:
CREATE EXTERNAL TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name
} ( [ ,�n ] )
WITH (
LOCATION = �folder_or_filepath�,
DATA_SOURCE = external_data_source_name, FILE_FORMAT = external_file_format_name Box 2. OPENROWSET
When using serverless SQL pool, CETAS is used to create an external table and export query results to Azure Storage Blob or Azure Data Lake Storage Gen2.
Example: AS
SELECT decennialTime, stateName, SUM(population) AS population FROM
OPENROWSET(BULK
�https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release/us_pop ulation_county/year=*/*.parquet�,
FORMAT=�PARQUET�) AS [r]
GROUP BY decennialTime, stateName GO

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References

Data Engineering on Microsoft Azure TestPrep
Data Engineering on Microsoft Azure Pass Guides
Data Engineering on Microsoft Azure practice questions
Data Engineering on Microsoft Azure Real exam Questions
Data Engineering on Microsoft Azure exam Cram
Data Engineering on Microsoft Azure PDF Download
Data Engineering on Microsoft Azure Latest Topics
Data Engineering on Microsoft Azure Study Guide

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Data Engineering on Microsoft Azure Practice Test

DP-203 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

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