Instantly download Databricks-Certified-Data-Engineer-Professional updated real questions

A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

• A. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
• B. Use a foreach task with a list of report types as its inputs.
• C. Define ten Notebook tasks to clearly track which report download failed.
• D. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.

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A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

• A. Use Unity Catalog's row filters based on the region and column masks based on user roles.
• B. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
• C. Create dynamic views for each user role and manage access with ACLs.
• D. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.

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A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

• A. Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
• B. Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
• C. Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
• D. Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
• E. Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.

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The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

• A. Can Manage
• B. Can Read
• C. Can Run
• D. No permissions
• E. Can Edit

A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

• A. Parse the Delta Lake transaction log to identify all newly written data files.
• B. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
• C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
• D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.

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