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Databricks Certified Data Engineer Professional Sample Questions:

1. The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A) Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
B) Group members have full permissions on the prod database and can also assign permissions to other users or groups.
C) Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
D) Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
E) Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

2. 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) 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) 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.

3. Which statement describes a key benefit of an end-to-end test?

A) It makes it easier to automate your test suite
B) It closely simulates real world usage of your application.
C) It provides testing coverage for all code paths and branches.
D) It pinpoint errors in the building blocks of your application.

4. The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A) Schedule a job to execute the pipeline once hour on a new job cluster.
B) Configure a job that executes every time new data lands in a given directory.
C) Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D) Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

5. The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A) Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
B) In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
C) All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
D) Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

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