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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider's CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider's CI/CD automation for this task?

A) init, deploy, run, validate
B) validate, deploy, run
C) init, validate, deploy, run
D) deploy, run, validate

2. 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 Edit
D) No permissions
E) Can Run

3. A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A) Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
B) Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
C) Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
D) Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.

4. The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When a database is being created, make sure that the LOCATION keyword is used.
B) When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
C) When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
D) When the workspace is being configured, make sure that external cloud object storage has been mounted.
E) When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

5. A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A) Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
B) Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
C) Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
D) Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.

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