A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

• A.Delta APPEND_ONLY
• B.Delta VACUUM
• C.Delta OPTIMIZE
• D.Delta Time Travel

Comment: *

Name: *

Email: *

Verification: *

Review the following error traceback:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which statement describes the error being raised?

• A.The code executed was PvSoark but was executed in a Scala notebook.
• B.There is no column in the table named heartrateheartrateheartrate
• C.There is a type error because a column object cannot be multiplied.
• D.There is a type error because a DataFrame object cannot be multiplied.
• E.There is a syntax error because the heartrate column is not correctly identified as a column.

Comment: *

Name: *

Email: *

Verification: *

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

Comment: *

Name: *

Email: *

Verification: *

A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

• A.def new_records():
• B.return spark.readStream.table("bronze")
• C.return spark.readStream.load("bronze")
• D.return spark.read.option("readChangeFeed", "true").table ("bronze")
• E.

Comment: *

Name: *

Email: *

Verification: *

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

• A.Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
• B.Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
• C.Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.
• D.Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
• E.Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.

Comment: *

Name: *

Email: *

Verification: *