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Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps
Certified Data Engineer Professional Questions 65–72 (page: 10)

Updated On: 16-Apr-2026
Page 10 of 44
PDF with 339 Questions

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?

  1. 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.
  2. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
  3. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
  4. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
  5. 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.

Answer(s): A



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?

  1. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
  2. 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.
  3. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
  4. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
  5. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Answer(s): E



The data science team has requested assistance in accelerating queries on free-form text from user reviews. The data is currently stored in Parquet with the below schema:

item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING

The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.

A junior data engineer suggests converting this data to Delta Lake will improve query performance.

Which response to the junior data engineer's suggestion is correct?

  1. Delta Lake statistics are not optimized for free text fields with high cardinality.
  2. Text data cannot be stored with Delta Lake.
  3. ZORDER ON review will need to be run to see performance gains.
  4. The Delta log creates a term matrix for free text fields to support selective filtering.
  5. Delta Lake statistics are only collected on the first 4 columns in a table.

Answer(s): A



Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

  1. configure
  2. fs
  3. jobs
  4. libraries
  5. workspace

Answer(s): B



The business intelligence team has a dashboard configured to track various summary metrics for retail stores. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:



For demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table, named products_per_order, includes the following fields:



Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.

Which solution meets the expectations of the end users while controlling and limiting possible costs?

  1. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
  2. Use Structured Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
  3. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
  4. Use the Delta Cache to persist the products_per_order table in memory to quickly update the dashboard with each query.
  5. Define a view against the products_per_order table and define the dashboard against this view.

Answer(s): A



A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target 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?

  1. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
  2. 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.
  3. 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.
  4. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
  5. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Answer(s): A



A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late- arriving data.

Streaming DataFrame df has the following schema:

"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"

Code block:



Choose the response that correctly fills in the blank within the code block to complete this task.

  1. withWatermark("event_time", "10 minutes")
  2. awaitArrival("event_time", "10 minutes")
  3. await("event_time + `10 minutes'")
  4. slidingWindow("event_time", "10 minutes")
  5. delayWrite("event_time", "10 minutes")

Answer(s): A



A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new promotion, and they would like to add a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows. Note that proposed changes are in bold.

Original query:



Proposed query:



Which step must also be completed to put the proposed query into production?

  1. Specify a new checkpointLocation
  2. Increase the shuffle partitions to account for additional aggregates
  3. Run REFRESH TABLE delta.'/item_agg'
  4. Register the data in the "/item_agg" directory to the Hive metastore
  5. Remove .option(`mergeSchema', `true') from the streaming write

Answer(s): A



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