A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

• A. overwriteSchema = true
• B. ignoreChanges = false
• C. validateSchema = false
• D. mergeSchema = true

A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

• A. Merge all changes back to the main branch in the remote Git repository and clone the repo again
• B. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
• C. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
• D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
• E. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake, they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join, whereas queries on legacy formats used Sort Merge Joins. The queries reading Delta Lake data also scanned less data. Which reason could be attributed to the difference in query performance?

• A. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.
• B. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
• C. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
• D. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

• A. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
• B. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
• C. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
• D. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

• A. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
• B. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
• C. The activity details table already exists; CHECK constraints can only be added during initial table creation.
• D. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
• E. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.