A data analyst needs to find all datasets in the Microsoft Purview data catalog that are related to 'customer' and have been updated in the last 30 days. The analyst also wants to understand the schema and see the lineage of these datasets. What should the analyst use in the Purview Data Catalog?

Use the search feature to search for 'customer', apply a filter for the last 30 days, and then browse the asset details to view schema and lineage.

Browse the data catalog by source type and manually look for customer-related assets updated in the last 30 days.

Create a custom report in Purview to list all assets and filter by 'customer' and last 30 days.

Use the Purview Atlas API to query for assets with 'customer' in the name and check the update time.

A retail company is building a new analytics solution in Azure Synapse Analytics. They need to model sales transactions, products, customers, and stores in a star schema. They want to start with a pre-built schema that follows industry best practices and can be customized. What should they use?

Use a SQL serverless database with external tables pointing to the data lake.

Create a new database using the Retail database template in Synapse Database Templates.

Use Azure Data Factory to copy data into an Azure SQL Database and create tables manually.

Build a custom database from scratch using SQL scripts in a dedicated SQL pool.

A data analyst needs to explore a new Parquet dataset in Azure Data Lake Storage Gen2. They want to run ad-hoc SQL queries without provisioning a dedicated pool, and they also need to see the schema and file-level metadata of the dataset before writing queries. What should they use?

Provision a dedicated SQL pool, load the Parquet data into a table, and then use the catalog to browse the table schema.

Use Azure Databricks to mount the storage and run SQL queries, then view metadata in the Databricks UI.

Use Azure Data Factory to copy the Parquet files into an Azure SQL Database and query them there.

Create a SQL serverless database in Azure Synapse Analytics and use OPENROWSET to query the Parquet files, then browse the file metadata in the Synapse workspace.

A data science team needs to run a complex machine learning feature engineering job on a large dataset. The job requires custom Python libraries and must scale out. The team also needs to find the output dataset in Microsoft Purview for governance. What should they do?

Use Azure Databricks with a cluster and then manually upload the output to Purview.

Use Azure Data Factory to run the job as a custom activity.

Use SQL serverless to run the feature engineering as a T-SQL query.

Use a Spark pool in Azure Synapse Analytics with a custom conda environment, and register the output in Purview.

Microsoft Certified:Azure Data Engineer Associate

Domain 1Objective 2

Design and Implement the Data Exploration Layer DP-203 Practice Questions (Page 5)

Part of the Design and implement data storage domain, which accounts for 15–20% of the DP-203 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~6–13 in this domain), expect 3–7 from this objective — we provide 38 practice questions to prepare you well beyond it. (estimate)

38questions here

8free pages

7concepts

15–20%of the exam

Questions 21–25

21application · medium
A data analyst needs to find all datasets in the Microsoft Purview data catalog that are related to 'customer' and have been updated in the last 30 days. The analyst also wants to understand the schema and see the lineage of these datasets. What should the analyst use in the Purview Data Catalog?
Select an answer first
22application · medium
A retail company is building a new analytics solution in Azure Synapse Analytics. They need to model sales transactions, products, customers, and stores in a star schema. They want to start with a pre-built schema that follows industry best practices and can be customized. What should they use?
Select an answer first
23foundation · easy
Which Azure Synapse Analytics component is specifically designed for on-demand T-SQL queries over data stored in Azure Data Lake Storage, without the need to manage or scale compute resources?
Select an answer first
24application · medium
A data analyst needs to explore a new Parquet dataset in Azure Data Lake Storage Gen2. They want to run ad-hoc SQL queries without provisioning a dedicated pool, and they also need to see the schema and file-level metadata of the dataset before writing queries. What should they use?
Select an answer first
25expert · hard
A data science team needs to run a complex machine learning feature engineering job on a large dataset. The job requires custom Python libraries and must scale out. The team also needs to find the output dataset in Microsoft Purview for governance. What should they do?
Select an answer first

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