A data engineering team uses Declarative Automation Bundles (formerly Databricks Asset Bundles) to deploy the same codebase across dev, test, and prod environments. The team wants environment-specific behavior to be applied only through bundle configuration, without modifying notebooks, job definitions, or deployment logic during promotion. Which approach applies environment-specific configuration while meeting this requirement?

• A. Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.
• B. Use separate Git branches per environment so each branch can contain environment-specific configuration values.
• C. Define bundle variables and reference them from bundle resources, then override variable values per environment using bundle targets.
• D. Parameterize environment-specific values inside notebooks and set those parameters when each job run is triggered.

A Python file is ready for production and the client wants to use the most efficient yet cost- effective type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations.
Which cluster meets the requirement?

• A. Interactive cluster
• B. Job cluster with spot instances disabled
• C. Job cluster with Photon enabled
• D. Job cluster with spot instances enabled

A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

• A. They can clone the existing task in the existing Job and update it to run the new notebook.
• B. They can create a new job from scratch and add both tasks to run concurrently.
• C. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
• D. They can clone the existing task to a new Job and then edit it to run the new notebook.
• E. They can create a new task in the existing Job and then add it as a dependency of the original task.

A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution. Which compute option should the data engineer use?

• A. Serverless SQL Warehouse
• B. Databricks Runtime for ML
• C. Databricks SQL Analytics
• D. Databricks Jobs

A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

• A. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
• B. They can set up DLT to notify them via email when records are dropped.
• C. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
• D. They cannot determine which table is dropping the records.
• E. They can set up separate expectations for each table when developing their DLT pipeline.