Study with Databricks-Certified-Data-Engineer-Professional most valid questions & verified answers

Which distribution does Databricks support for installing custom Python code packages?

• A. jars
• B. CRAN
• C. sbt
• D. nom
• E. CRAM
• F. Wheels

A table in the Lakehouse named customer_churn_params 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.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

• A. Modify the overwrite logic to include a field populated by calling
  spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
• B. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
• C. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
• D. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
• E. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

• A. Post_time
• B. Date
• C. latitude
• D. User_id
• E. Post_id

A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

• A. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.
• B. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
• C. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.
• D. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.

Review the following error traceback:

Which statement describes the error being raised?

• A. There is a type error because a column object cannot be multiplied.
• B. There is no column in the table named heartrateheartrateheartrate
• C. There is a type error because a DataFrame object cannot be multiplied.
• D. There is a syntax error because the heartrate column is not correctly identified as a column.
• E. The code executed was PvSoark but was executed in a Scala notebook.