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Databricks Certified Data Engineer Professional Sample Questions:

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

2. A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from part- file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
C) Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
D) Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
E) Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

3. A table is registered with the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
D) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.

4. When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A) Total Disk Space remains constant
B) Bytes Received never exceeds 80 million bytes per second
C) Network I/O never spikes
D) The five Minute Load Average remains consistent/flat
E) CPU Utilization is around 75% Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

5. Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A) DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B) The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
C) Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
D) The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
E) By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.

Solutions: