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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. Updating the table schema will invalidate the Delta transaction log metadata.
B. Spark cannot capture the topic partition fields from the kafka source.
C. New fields cannot be added to a production Delta table.
D. Updating the table schema requires a default value provided for each file added.
E. New fields will not be computed for historic records.

Question 2

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

Question 3

The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:

What does the data governance team need to do next to achieve this goal?

A. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
B. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
C. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
D. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);

Question 4

The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
C. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
D. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.

Question 5

A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A. Yes; Delta Lake supports infinite concurrent writers.
B. No; only one stream can write to a Delta Lake table.
C. Yes; both of the streams can share a single checkpoint directory.
D. No; each of the streams needs to have its own checkpoint directory.
E. No; Delta Lake manages streaming checkpoints in the transaction log.

Solutions: