Question 1

A DLT pipeline includes the following streaming tables:

• raw_iot ingests raw device measurement data from a heart rate tracking device.

• bpm_stats incrementally computes user statistics based on BPM measurements from raw_iot.

How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table, while recomputing the downstream table bpm_stats table when a pipeline update is run?

Options :

A : Set the pipelines.reset.allowed property to false on raw_iot

B : Set the skipChangeCommits flag to true on raw_iot

C : Set the pipelines.reset.allowed property to false on bpm_stats

D : Set the skipChangeCommits flag to true on bpm_stats

Answer: B

Question 2

A data engineer has noticed the comment ‘# Databricks notebook source’ on the first line of each Databricks Python file’s source code pushed to Github.

Which of the following explain the purpose of this comment ?

Options :

A : This comment makes it easier for humans to understand the source of the generated code from Databricks

B : This comment establishes the Python files as Databricks notebooks

C : This comment is used for Python auto-generated documentation

D : This comment add the Python file to the search index in Databricks workspace

E : There is no special purpose for this comment. Comments in Python are ignored by the interpreter.

Answer: B

Question 3

Given the following code block in a notebook

db_password = dbutils.secrets.get(scope="dev", key="database_password")

print (db_password)

Which statement describes what will happen when the above code is executed?

Options :

A : An interactive input box will appear in the notebook

B : The string "REDACTED" will be printed.

C : The error message “Secrets can not be printed” will be shown

D : The string value of the password will be printed in plain text.

E : If the user has “Can Read” permission, the string value of the password will be printed in plain text. Otherwise, the string "REDACTED" will be printed

Answer: B

Question 4

A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

Options :

A :

Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.

B :

Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

C :

Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.

D :

Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: B

Question 5

Which of the following establishes a Python file as a notebook in Databricks ?

Options :

A : The magic command %databricks on the first line of the file’s source code

B : The comment ‘# Databricks notebook source’ on the first line of the file’s source code

C : The import of the dbutils.notebook module in the file’s source code

D : The creation of a spark session using SparkSession.builder.getOrCreate() in the file’s source code

E : None of the above statements is correct. Notebooks are binary files with .pydb file extension.

Answer: B

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