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Microsoft DP-203 (Data Engineering on Microsoft Azure) Exam is a certification that validates a professional's knowledge and skills in designing and implementing data solutions on Microsoft Azure. DP-203 exam measures an individual's ability to perform tasks such as data storage, processing, and security using Azure technologies. To pass DP-203 exam, candidates must have a comprehensive understanding of Azure data services, including Azure Data Factory, Azure SQL Database, and Azure Cosmos DB.
Microsoft DP-203: Data Engineering on Microsoft Azure exam is an important certification for professionals working in the field of data engineering. It tests the candidate’s skills and knowledge in designing and implementing data solutions using Azure services. Candidates can prepare for the exam by taking the official Microsoft training course or by using study materials available online. Upon passing the exam, candidates will earn the Microsoft Certified: Azure Data Engineer Associate certification, which can lead to better job opportunities and higher salaries.
NEW QUESTION # 26
You need to collect application metrics, streaming query events, and application log messages for an Azure Databrick cluster.
Which type of library and workspace should you implement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
You can send application logs and metrics from Azure Databricks to a Log Analytics workspace. It uses the Azure Databricks Monitoring Library, which is available on GitHub.
References:
https://docs.microsoft.com/en-us/azure/architecture/databricks-monitoring/application-logs
NEW QUESTION # 27
You are designing a star schema for a dataset that contains records of online orders. Each record includes an order date, an order due date, and an order ship date.
You need to ensure that the design provides the fastest query times of the records when querying for arbitrary date ranges and aggregating by fiscal calendar attributes.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Use built-in SQL functions to extract date attributes.
- B. Create a date dimension table that has an integer key in the format of yyyymmdd.
- C. Create a date dimension table that has a DateTime key.
- D. Use DateTime columns for the date fields.
- E. In the fact table, use integer columns for the date fields.
Answer: A,E
NEW QUESTION # 28
You need to implement a Type 3 slowly changing dimension (SCD) for product category data in an Azure Synapse Analytics dedicated SQL pool.
You have a table that was created by using the following Transact-SQL statement.
Which two columns should you add to the table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. [EffectiveScarcDate] [datetime] NOT NULL,
- B. [ProductCategory] [nvarchar] (100) NOT NULL,
- C. [EffectiveEndDace] [dacecime] NULL,
- D. [OriginalProduccCacegory] [nvarchar] (100) NOT NULL,
- E. [CurrentProduccCacegory] [nvarchar] (100) NOT NULL,
Answer: D,E
Explanation:
Explanation
A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.
This type of tracking may be used for one or two columns in a dimension table. It is not common to use it for many members of the same table. It is often used in combination with Type 1 or Type 2 members.
Graphical user interface, application, email Description automatically generated
Reference:
https://k21academy.com/microsoft-azure/azure-data-engineer-dp203-q-a-day-2-live-session-review/
NEW QUESTION # 29
You have an Azure Synapse Analytics dedicated SQL pool that contains the users shown in the following table.
User1 executes a query on the database, and the query returns the results shown in the following exhibit.
User1 is the only user who has access to the unmasked data.
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.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/dynamic-data-masking-overview
NEW QUESTION # 30
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:
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.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables
NEW QUESTION # 31
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model Pool1 will contain the following tables.
Answer:
Explanation:
NEW QUESTION # 32
You plan to use an Apache Spark pool in Azure Synapse Analytics to load data to an Azure Data Lake Storage Gen2 account.
You need to recommend which file format to use to store the data in the Data Lake Storage account. The solution must meet the following requirements:
* Column names and data types must be defined within the files loaded to the Data Lake Storage account.
* Data must be accessible by using queries from an Azure Synapse Analytics serverless SQL pool.
* Partition elimination must be supported without having to specify a specific partition.
What should you recommend?
- A. Delta Lake
- B. CSV
- C. JSON
- D. ORC
Answer: D
NEW QUESTION # 33
You develop a dataset named DBTBL1 by using Azure Databricks.
DBTBL1 contains the following columns:
SensorTypeID
GeographyRegionID
Year
Month
Day
Hour
Minute
Temperature
WindSpeed
Other
You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID.
The solution must minimize storage costs.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
NEW QUESTION # 34
You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks.
A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
* A destination table in Azure Synapse
* An Azure Blob storage container
* A service principal
In which order should you perform the actions? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Table Description automatically generated
Step 1: Mount the Data Lake Storage onto DBFS
Begin with creating a file system in the Azure Data Lake Storage Gen2 account.
Step 2: Read the file into a data frame.
You can load the json files as a data frame in Azure Databricks.
Step 3: Perform transformations on the data frame.
Step 4: Specify a temporary folder to stage the data
Specify a temporary folder to use while moving data between Azure Databricks and Azure Synapse.
Step 5: Write the results to a table in Azure Synapse.
You upload the transformed data frame into Azure Synapse. You use the Azure Synapse connector for Azure Databricks to directly upload a dataframe as a table in a Azure Synapse.
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
NEW QUESTION # 35
You have a data warehouse in Azure Synapse Analytics.
You need to ensure that the data in the data warehouse is encrypted at rest.
What should you enable?
- A. Transparent Data Encryption (TDE)
- B. Secure transfer required
- C. Dynamic Data Masking
- D. Advanced Data Security for this database
Answer: A
Explanation:
Azure SQL Database currently supports encryption at rest for Microsoft-managed service side and client-side encryption scenarios.
Support for server encryption is currently provided through the SQL feature called Transparent Data Encryption.
Client-side encryption of Azure SQL Database data is supported through the Always Encrypted feature.
Reference:
https://docs.microsoft.com/en-us/azure/security/fundamentals/encryption-atrest
NEW QUESTION # 36
A company plans to use Platform-as-a-Service (PaaS) to create the new data pipeline process. The process must meet the following requirements:
Ingest:
Access multiple data sources.
Provide the ability to orchestrate workflow.
Provide the capability to run SQL Server Integration Services packages.
Store:
Optimize storage for big data workloads.
Provide encryption of data at rest.
Operate with no size limits.
Prepare and Train:
Provide a fully-managed and interactive workspace for exploration and visualization.
Provide the ability to program in R, SQL, Python, Scala, and Java.
Provide seamless user authentication with Azure Active Directory.
Model & Serve:
Implement native columnar storage.
Support for the SQL language
Provide support for structured streaming.
You need to build the data integration pipeline.
Which technologies should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 37
You plan to create a dimension table in Azure Synapse Analytics that will be less than 1 GB.
You need to create the table to meet the following requirements:
* Provide the fastest Query time.
* Minimize data movement during queries.
Which type of table should you use?
- A. replicated
- B. round-robin
- C. hash distributed
- D. heap
Answer: A
NEW QUESTION # 38
You have the following Azure Stream Analytics query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-in/blog/maximize-throughput-with-repartitioning-in-azure-stream-analytics/
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption
NEW QUESTION # 39
You have an Azure subscription that contains an Azure Synapse Analytics dedicated SQL pool named Pool1.
Pool1 receives new data once every 24 hours.
You have the following function.
You have the following query.
The query is executed once every 15 minutes and the @parameter value is set to the current date.
You need to minimize the time it takes for the query to return results.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Change the table distribution to replicate.
- B. Enable result set caching.
- C. Create an index on the avg_f column.
- D. Convert the avg_c column into a calculated column.
- E. Create an index on the sensorid column.
Answer: B,D
Explanation:
Explanation
https://learn.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-result-set-cach
NEW QUESTION # 40
You have an Azure subscription that contains an Azure Synapse Analytics workspace named workspace1. Workspace1 contains a dedicated SQL pool named SQL Pool and an Apache Spark pool named sparkpool. Sparkpool1 contains a DataFrame named pyspark.df.
You need to write the contents of pyspark_df to a tabte in SQLPooM by using a PySpark notebook.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 41
You need to design a solution that will process streaming data from an Azure Event Hub and output the data to Azure Data Lake Storage. The solution must ensure that analysts can interactively query the streaming data.
What should you use?
- A. event triggers in Azure Data Factory
- B. Azure Queue storage and read-access geo-redundant storage (RA-GRS)
- C. Azure Stream Analytics and Azure Synapse notebooks
- D. Structured Streaming in Azure Databricks
Answer: D
Explanation:
Apache Spark Structured Streaming is a fast, scalable, and fault-tolerant stream processing API. You can use it to perform analytics on your streaming data in near real-time.
With Structured Streaming, you can use SQL queries to process streaming data in the same way that you would process static data.
Azure Event Hubs is a scalable real-time data ingestion service that processes millions of data in a matter of seconds. It can receive large amounts of data from multiple sources and stream the prepared data to Azure Data Lake or Azure Blob storage.
Azure Event Hubs can be integrated with Spark Structured Streaming to perform the processing of messages in near real-time. You can query and analyze the processed data as it comes by using a Structured Streaming query and Spark SQL.
NEW QUESTION # 42
You are processing streaming data from vehicles that pass through a toll booth.
You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics
NEW QUESTION # 43
You are planning a solution to aggregate streaming data that originates in Apache Kafka and is output to Azure Data Lake Storage Gen2. The developers who will implement the stream processing solution use Java, Which service should you recommend using to process the streaming data?
- A. Azure Event Hubs
- B. Azure Stream Analytics
- C. Azure Databricks
- D. Azure Data Factory
Answer: C
Explanation:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/stream-processing
NEW QUESTION # 44
You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container.
Which resource provider should you enable?
- A. Microsoft.Sql
- B. Microsoft-Automation
- C. Microsoft.EventGrid
- D. Microsoft.EventHub
Answer: C
NEW QUESTION # 45
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Microsoft DP-203 exam is a challenging exam that requires in-depth knowledge of data engineering on the Azure platform. Candidates are expected to have a solid understanding of data engineering concepts, including data modeling, data ingestion, data processing, and data analysis. They should also be familiar with Azure data services, including Azure SQL Database, Azure SQL Managed Instance, Azure HDInsight, and Azure Data Lake Analytics.
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