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Databricks Certified Data Engineer Professional Exam Sample Questions (Q99-Q104):

NEW QUESTION # 99
Which statement describes Delta Lake optimized writes?

A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
C. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

Answer: C

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.

NEW QUESTION # 100
The following table consists of items found in user carts within an e-commerce website.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The update throws an error because changes to existing columns in the target schema are not supported.
B. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
C. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
D. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.

Answer: B

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

NEW QUESTION # 101
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Answer: A

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 102
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. import sys
date = sys.argv[1]
B. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
C. date = spark.conf.get("date")
D. date = dbutils.notebooks.getParam("date")
E. input_dict = input()
date= input_dict["date"]

Answer: B

Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter.
For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.

NEW QUESTION # 103
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Perform a full outer join on a unique key and overwrite existing data.
B. VACUUM the Delta table after each batch completes.
C. Perform an insert-only merge with a matching condition on a unique key.
D. Rely on Delta Lake schema enforcement to prevent duplicate records.
E. Set the configuration delta.deduplicate = true.

Answer: C

Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

NEW QUESTION # 104
......

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