Question # 1
| The DevOps team has configured a production workload as a collection of notebooks
scheduled to run daily using the Jobs UI. A new data engineering hire is onboarding to the
team and has requested access to one of these notebooks to review the production logic.
What are the maximum notebook permissions that can be granted to the user without
allowing accidental changes to production code or data?
| | A. Can Manage | | B. Can Edit | | C. No permissions | | D. Can Read | | E. Can Run |
C. No permissions
Explanation:
This is the correct answer because it is the maximum notebook permissions
that can be granted to the user without allowing accidental changes to production code or
data. Notebook permissions are used to control access to notebooks in Databricks
workspaces. There are four types of notebook permissions: Can Manage, Can Edit, Can
Run, and Can Read. Can Manage allows full control over the notebook, including editing,
running, deleting, exporting, and changing permissions. Can Edit allows modifying and
running the notebook, but not changing permissions or deleting it. Can Run allows
executing commands in an existing cluster attached to the notebook, but not modifying or
exporting it. Can Read allows viewing the notebook content, but not running or modifying it.
In this case, granting Can Read permission to the user will allow them to review the
production logic in the notebook without allowing them to make any changes to it or run any
commands that may affect production data. Verified References: [Databricks Certified Data
Engineer Professional], under “Databricks Workspace” section; Databricks Documentation,
under “Notebook permissions” section.
Question # 2
| A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to
create a Type 1 table representing all of the values that have ever been valid for all rows in
a bronze table created with the property delta.enableChangeDataFeed = true. They plan to
execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple
times?
| | A. Each time the job is executed, newly updated records will be merged into the target
table, overwriting previous values with the same primary keys. | | B. Each time the job is executed, the entire available history of inserted or updated records
will be appended to the target table, resulting in many duplicate entries. | | C. Each time the job is executed, the target table will be overwritten using the entire history
of inserted or updated records, giving the desired result. | | D. Each time the job is executed, the differences between the original and current versions
are calculated; this may result in duplicate entries for some records. | | E. Each time the job is executed, only those records that have been inserted or updated
since the last execution will be appended to the target table giving the desired result. |
B. Each time the job is executed, the entire available history of inserted or updated records
will be appended to the target table, resulting in many duplicate entries.
Explanation: Reading table’s changes, captured by CDF, using spark.read means that you
are reading them as a static source. So, each time you run the query, all table’s changes
(starting from the specified startingVersion) will be read.
Question # 3
| The marketing team is looking to share data in an aggregate table with the sales
organization, but the field names used by the teams do not match, and a number of
marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity? | | A. Create a view on the marketing table selecting only these fields approved for the sales
team alias the names of any fields that should be standardized to the sales naming
conventions. | | B. Use a CTAS statement to create a derivative table from the marketing table configure a
production jon to propagation changes. | | C. Add a parallel table write to the current production pipeline, updating a new sales table
that varies as required from marketing table. | | D. Create a new table with the required schema and use Delta Lake's DEEP CLONE
functionality to sync up changes committed to one table to the corresponding table. |
A. Create a view on the marketing table selecting only these fields approved for the sales
team alias the names of any fields that should be standardized to the sales naming
conventions.
Explanation:
Creating a view is a straightforward solution that can address the need for
field name standardization and selective field sharing between departments. A view allows
for presenting a transformed version of the underlying data without duplicating it. In this
scenario, the view would only include the approved fields for the sales team and rename
any fields as per their naming conventions.
References:
Databricks documentation on using SQL views in Delta Lake:
https://docs.databricks.com/delta/quick-start.html#sql-views
Question # 4
| Which of the following technologies can be used to identify key areas of text when parsing
Spark Driver log4j output? | | A. Regex | | B. Julia | | C. pyspsark.ml.feature | | D. Scala Datasets | | E. C++ |
A. Regex
Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in
text. They can be used to identify key areas of text when parsing Spark Driver log4j output,
such as the log level, the timestamp, the thread name, the class name, the method name,
and the message. Regex can be applied in various languages and frameworks, such as
Scala, Python, Java, Spark SQL, and Databricks notebooks.
References:
https://docs.databricks.com/notebooks/notebooks-use.html#use-regularexpressions
https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regularexpressions-in-udfs
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html
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
| The data governance team has instituted a requirement that all tables containing Personal
Identifiable Information (PH) must be clearly annotated. This includes adding column
comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been
met? | | A. DESCRIBE EXTENDED dev.pii test | | B. DESCRIBE DETAIL dev.pii test | | C. SHOW TBLPROPERTIES dev.pii test | | D. DESCRIBE HISTORY dev.pii test | | E. SHOW TABLES dev |
A. DESCRIBE EXTENDED dev.pii test
Explanation:
This is the correct answer because it allows manual confirmation that these
three requirements have been met. The requirements are that all tables containing
Personal Identifiable Information (PII) must be clearly annotated, which includes adding
column comments, table comments, and setting the custom table property “contains_pii” =
true.
The DESCRIBE EXTENDED command is used to display detailed information about a
table, such as its schema, location, properties, and comments. By using this command on
the dev.pii_test table, one can verify that the table has been created with the correct
column comments, table comment, and custom table property as specified in the SQL DDL
statement.
Verified References: [Databricks Certified Data Engineer Professional], under
“Lakehouse” section; Databricks Documentation, under “DESCRIBE EXTENDED” section.
Question # 7
| Which statement describes Delta Lake optimized writes? | | A. 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. | | B. Optimized writes logical partitions instead of directory partitions partition boundaries are
only represented in metadata fewer small files are written. | | C. 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. | | D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the
most recent job. |
A. 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.
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.
References:
Databricks documentation on Delta Lake performance tuning:
https://docs.databricks.com/delta/optimizations/auto-optimize.html
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FAQs of Databricks-Certified-Professional-Data-Engineer Exam
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There
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