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Databricks Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A junior data engineer needs to create a Spark SQL table my_table for which Spark manages both the data and
the metadata. The metadata and data should also be stored in the Databricks Filesystem (DBFS).
Which of the following commands should a senior data engineer share with the junior data engineer to
complete this task?

A. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path");
B. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
C. 1. CREATE TABLE my_table (id STRING, value STRING);
D. 1. CREATE TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path")
E. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);

Answer: C

NEW QUESTION # 105
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
E. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Answer: E

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 106
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

A. Control plane
B. Databricks web application
C. JDBC data source
D. Data plane
E. Databricks Filesystem

Answer: D

Explanation:
Explanation
The answer is Data Plane, which is where compute(all-purpose, Job Cluster, DLT) are stored this is generally a customer cloud account, there is one exception SQL Warehouses, currently there are 3 types of SQL Warehouse compute available(classic, pro, serverless), in classic and pro compute is located in customer cloud account but serverless computed is located in Databricks cloud account.
Diagram, timeline Description automatically generated

NEW QUESTION # 107
Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
B. The checkpoint directory will be used to track updates to the static Delta table.
C. The checkpoint directory will be used to track state information for the unique keys present in the join.
D. Stream-static joins cannot use static Delta tables because of consistency issues.
E. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.

Answer: A

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 108
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table

Answer: B

Explanation:
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.
https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

NEW QUESTION # 109
......

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