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Databricks Certified Data Engineer Professional Sample Questions:

1. A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
B) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
C) Use repos to merge all difference and make a pull request back to the remote repository.
D) Use Repos to merge all differences and make a pull request back to the remote repository.
E) Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

2. 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 ID and its OAuth client secret.
C) Use a shared user account and its OAuth client secret.
D) Use a service principal and its Personal Access Token.

3. A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A) df.withColumn ("user_emai", F.expr("uuid()"))
B) df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
C) df.withColumn ("user_email", F.sha2 ("user_email"))
D) df.withColumn ("hashed_email", sha2 ("user_email"))

4. A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.
C) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
D) When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.

5. Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A) Validates a complete use case of your application
B) Improves the quality of your data
C) Yields faster deployment and execution times
D) Ensures that all steps interact correctly to achieve the desired end result
E) Troubleshooting is easier since all steps are isolated and tested individually

Solutions: