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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?

A) Use a service principal with OAuth token federation.
B) Use a service principal and its Personal Access Token.
C) Use a service principal ID and its OAuth client secret.
D) Use a shared user account and its OAuth client secret.

2. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
D) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

3. A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A) The Delta log is scanned for min and max statistics for the latitude column
B) All records are cached to an operational database and then the filter is applied
C) The Parquet file footers are scanned for min and max statistics for the latitude column
D) The Hive metastore is scanned for min and max statistics for the latitude column
E) All records are cached to attached storage and then the filter is applied

4. 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) 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
B) Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
C) Merge all changes back to the main branch in the remote Git repository and clone the repo again
D) Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
E) Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9

5. 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) 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.
B) 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.
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) 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.
E) 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.

Solutions: