A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected.
Which command will trigger the job execution?
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?
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:
The table daily_store_sales contains all the information needed to update store_sales_summary . The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
df has the following schema: device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT
Code block:
df.withWatermark( " event_time " , " 10 minutes " )
.groupBy(
________,
" device_id "
)
.agg(
avg( " temp " ).alias( " avg_temp " ),
avg( " humidity " ).alias( " avg_humidity " )
)
.writeStream
.format( " delta " )
.saveAsTable( " sensor_avg " )
Which line of code correctly fills in the blank within the code block to complete this task?
A data architect has heard about lake ' s built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
return spark.readStream.table( " bronze " )
return spark.readStream.load( " bronze " )
return spark.read.option( " readChangeFeed " , " true " ).table ( " bronze " )
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data.
Which configuration will provide the optimal performance while enabling these capabilities?
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?
A data engineer needs to implement column masking for a sensitive column in a Unity Catalog-managed table. The masking logic must dynamically check if users belong to specific groups defined in a separate table (group_access) that maps groups to allowed departments.
Which approach should the engineer use to efficiently enforce this requirement?
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for 15 days (instead of the default 7 days), in order to permanently comply with the organization’s data retention policy.
Which code snippet correctly sets this retention period for deleted files?
TESTED 29 Apr 2026
