Question # 1
| 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? | | A. configure | | B. fs | | C. jobs | | D. libraries | | E. workspace |
B. fs
Explanation:
The libraries command group allows you to install, uninstall, and list libraries
on Databricks clusters. You can use the libraries install command to install a custom
Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For
example, you can use the following command to install a custom Python Wheel named
mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a
production job. You can also use the libraries uninstall command to uninstall a library from
a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
Libraries CLI (legacy):
https://docs.databricks.com/en/archive/devtools/cli/libraries-cli.html
Library operations:
https://docs.databricks.com/en/devtools/cli/commands.html#library-operations
Install or update the Databricks CLI:
https://docs.databricks.com/en/devtools/cli/install.html
Question # 2
| The data engineering team has configured a job to process customer requests to be
forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta
Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at
1am each Sunday. The total duration of this job is less than one hour. Every Monday at
3am, a batch job executes a series of VACUUM commands on all Delta Lake tables
throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality.
They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses
this concern? | | A. Because the vacuum command permanently deletes all files containing deleted records,
deleted records may be accessible with time travel for around 24 hours. | | B. Because the default data retention threshold is 24 hours, data files containing deleted
records will be retained until the vacuum job is run the following day. | | C. Because Delta Lake time travel provides full access to the entire history of a table,
deleted records can always be recreated by users with full admin privileges. | | D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be
permanently purged from all storage systems as soon as a delete job completes. | | E. Because the default data retention threshold is 7 days, data files containing deleted
records will be retained until the vacuum job is run 8 days later. |
E. Because the default data retention threshold is 7 days, data files containing deleted
records will be retained until the vacuum job is run 8 days later.
Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum
Question # 3
| A data engineer needs to capture pipeline settings from an existing in the workspace, and
use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the
Databricks CLI?
| | A. Use the get command to capture the settings for the existing pipeline; remove the
pipeline_id and rename the pipeline; use this in a create command | | B. Stop the existing pipeline; use the returned settings in a reset command | | C. Use the alone command to create a copy of an existing pipeline; use the get JSON
command to get the pipeline definition; save this to git | | D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return
results parse and use this to create a pipeline |
A. Use the get command to capture the settings for the existing pipeline; remove the
pipeline_id and rename the pipeline; use this in a create command
Explanation:
The Databricks CLI provides a way to automate interactions with Databricks
services. When dealing with pipelines, you can use the databricks pipelines get --
pipeline-id command to capture the settings of an existing pipeline in JSON format. This
JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming
the pipeline to create a new pipeline. The modified JSON file can then be used with the
databricks pipelines create command to create a new pipeline with those settings.
References:
Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines
Question # 4
| A table is registered with the following code:
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. 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. | | C. 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. | | 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. |
B. 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.
Question # 5
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction
by the machine learning team. The table contains information about customers derived
from a number of upstream sources. Currently, the data engineering team populates this
table nightly by overwriting the table with the current valid values derived from upstream
data sources.
Immediately after each update succeeds, the data engineer team would like to determine
the difference between the new version and the previous of the table.
Given the current implementation, which method can be used? | | A. Parse the Delta Lake transaction log to identify all newly written data files. | | B. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation
metrics for the update, including a log of all records that have been added or modified. | | C. Execute a query to calculate the difference between the new version and the previous
version using Delta Lake’s built-in versioning and time travel functionality. | | D. Parse the Spark event logs to identify those rows that were updated, inserted, or
deleted. |
C. Execute a query to calculate the difference between the new version and the previous
version using Delta Lake’s built-in versioning and time travel functionality.
Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing
users to query previous snapshots of a table. This feature is particularly useful for
understanding changes between different versions of the table. In this scenario, where the
table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query
comparing the latest version of the table (the current state) with its previous version. This
approach effectively identifies the differences (such as new, updated, or deleted records)
between the two versions. The other options do not provide a straightforward or efficient
way to directly compare different versions of a Delta Lake table.
References:
Delta Lake Documentation on Time Travel: Delta Time Travel
Delta Lake Versioning: Delta Lake Versioning Guide
Question # 6
| A Data engineer wants to run unit’s tests using common Python testing frameworks on
python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production? | | A. Run unit tests against non-production data that closely mirrors production | | B. Define and unit test functions using Files in Repos | | C. Define units test and functions within the same notebook | | D. Define and import unit test functions from a separate Databricks notebook |
A. Run unit tests against non-production data that closely mirrors production
Explanation:
The best practice for running unit tests on functions that interact with data is
to use a dataset that closely mirrors the production data. This approach allows data
engineers to validate the logic of their functions without the risk of affecting the actual
production data. It's important to have a representative sample of production data to catch
edge cases and ensure the functions will work correctly when used in a production
environment.
References:
Databricks Documentation on Testing: Testing and Validation of Data and
Notebooks
Question # 7
| A table in the Lakehouse named customer_churn_params is used in churn prediction by
the machine learning team. The table contains information about customers derived from a
number of upstream sources. Currently, the data engineering team populates this table
nightly by overwriting the table with the current valid values derived from upstream data
sources.
The churn prediction model used by the ML team is fairly stable in production. The team is
only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records? | | A. Apply the churn model to all rows in the customer_churn_params table, but implement
logic to perform an upsert into the predictions table that ignores rows where predictions
have not changed. | | B. Convert the batch job to a Structured Streaming job using the complete output mode;
configure a Structured Streaming job to read from the customer_churn_params table and
incrementally predict against the churn model. | | C. Calculate the difference between the previous model predictions and the current
customer_churn_params on a key identifying unique customers before making new
predictions; only make predictions on those customers not in the previous predictions. | | D. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify
records written on a particular date. | | E. Replace the current overwrite logic with a merge statement to modify only those records
that have changed; write logic to make predictions on the changed records identified by the
change data feed. |
E. Replace the current overwrite logic with a merge statement to modify only those records
that have changed; write logic to make predictions on the changed records identified by the
change data feed.
Explanation:
The approach that would simplify the identification of the changed records is
to replace the current overwrite logic with a merge statement to modify only those records
that have changed, and write logic to make predictions on the changed records identified
by the change data feed. This approach leverages the Delta Lake features of merge and
change data feed, which are designed to handle upserts and track row-level changes in a
Delta table12. By using merge, the data engineering team can avoid overwriting the entire
table every night, and only update or insert the records that have changed in the source
data. By using change data feed, the ML team can easily access the change events that
have occurred in the customer_churn_params table, and filter them by operation type
(update or insert) and timestamp. This way, they can only make predictions on the records
that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
Option A would require applying the churn model to all rows in the
customer_churn_params table, which would be wasteful and redundant. It would
also require implementing logic to perform an upsert into the predictions table,
which would be more complex than using the merge statement.
Option B would require converting the batch job to a Structured Streaming job,
which would involve changing the data ingestion and processing logic. It would
also require using the complete output mode, which would output the entire result
table every time there is a change in the source data, which would be inefficient
and costly.
Option C would require calculating the difference between the previous model
predictions and the current customer_churn_params on a key identifying unique
customers, which would be computationally expensive and prone to errors. It
would also require storing and accessing the previous predictions, which would
add extra storage and I/O costs.
Option D would require modifying the overwrite logic to include a field populated by
calling spark.sql.functions.current_timestamp() as data are being written, which
would add extra complexity and overhead to the data engineering job. It would
also require using this field to identify records written on a particular date, which
would be less accurate and reliable than using the change data feed.
References: Merge, Change data feed
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FAQs of Databricks-Certified-Professional-Data-Engineer Exam
What
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This
exam assesses your ability to use Databricks to perform advanced data engineering tasks,
such as building pipelines, data modelling, and working with tools like Apache
Spark and Delta Lake.
Who
should take this exam?
Ideal
candidates are data engineers with at least one year of experience in relevant
areas and a strong understanding of the Databricks platform.
Is
there any required training before taking the exam?
There
are no prerequisites, but Databricks recommends relevant training to ensure
success.
What
is covered in the Databricks Certified Professional Data Engineer exam?
The
exam covers data ingestion, processing, analytics, and visualization using Databricks,
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Does
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The
exam focuses on core functionalities, but for optimal performance, it is
recommended that you be familiar with the latest versions. For the latest
features, refer to Databricks documentation: https://docs.databricks.com/en/release-notes/product/index.html.
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The
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While
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libraries like Koalas and MLflow can be beneficial. For notebooks and
libraries, refer to Databricks documentation: https://docs.databricks.com/en/notebooks/index.html.
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