Question 1

Which statement regarding Spark configuration on the Databricks platform is true?

Options :

A : The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs currently running on the cluster.

B : Spark configurations set within a notebook will affect all SparkSessions attached to the same interactive cluster.

C : When the same Spark configuration property is set for an interactive cluster and a notebook attached to that cluster, the notebook setting will always be ignored.

D : Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

Answer: D

Question 2

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 3

Which of the following statements correctly describes the sys.path Python variable ?

Options :

A : The sys.path variable contains a list of all the parameters passed to a Python notebook.

B : The sys.path variable contains a list of all the necessary dependencies for a Python notebook.

C : The sys.path variable contains a list of directories where the Python interpreter searches for modules.

D : The sys.path variable contains the full pathname of the current working directory of a Python notebook.

E : The sys.path variable is an alias for os.path

Answer: C

Question 4

A junior data engineer is using the following code to de-duplicate raw streaming data and insert them in a target Delta table

1. spark.readStream

2. .table("orders_raw")

3. .dropDuplicates(["order_id", "order_timestamp"])

4. .writeStream

5. .option("checkpointLocation", "dbfs:/checkpoints")

6. .table("orders_unique")

A senior data engineer pointed out that this approach is not enough for having distinct records in the target table when there are late-arriving, duplicate records.

Which of the following could explain the senior data engineer’s remark?

Options :

A : Watermarking is also needed to only track state information for a window of time in which we expect records could be delayed.

B : A ranking function is also needed to ensure processing only the most recent records

C : A window function is also needed to apply deduplication for each non-overlapping interval.

D : The new records need also to be deduplicated against previously inserted data into the table.

E : More information is needed to determine the correct response

Answer: D

Question 5

What is true for Delta Lake?

Options :

A : Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

B : Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

C : Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

D : Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

E : Z-order can only be applied to numeric values stored in Delta Lake tables.

Answer: B

Confidently Practice Online with Free Databricks-Certified-Professional-Data-Engineer Exam Cram

Buying Options: