Question # 1
| A new data engineer notices that a critical field was omitted from an application that writes
its Kafka source to Delta Lake. This happened even though the critical field was in the
Kafka source. That field was further missing from data written to dependent, long-term
storage. The retention threshold on the Kafka service is seven days. The pipeline has been
in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future? | | A. The Delta log and Structured Streaming checkpoints record the full history of the Kafka
producer. | | B. Delta Lake schema evolution can retroactively calculate the correct value for newly
added fields, as long as the data was in the original source. | | C. Delta Lake automatically checks that all fields present in the source data are included in
the ingestion layer. | | D. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not
possible under any circumstance. | | E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a
permanent, replayable history of the data state. |
E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a
permanent, replayable history of the data state.
Explanation:
This is the correct answer because it describes how Delta Lake can help to
avoid data loss of this nature in the future. By ingesting all raw data and metadata from
Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the
data state that can be used for recovery or reprocessing in case of errors or omissions in
downstream applications or pipelines. Delta Lake also supports schema evolution, which
allows adding new columns to existing tables without affecting existing queries or pipelines.
Therefore, if a critical field was omitted from an application that writes its Kafka source to
Delta Lake, it can be easily added later and the data can be reprocessed from the bronze
table without losing any information. Verified References: [Databricks Certified Data
Engineer Professional], under “Delta Lake” section; Databricks Documentation, under
“Delta Lake core features” section.
Question # 2
| A user wants to use DLT expectations to validate that a derived table report contains all
records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table
definition.
Which approach would allow using DLT expectations to validate all expected records are
present in this table?
| | A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns
null values for report key values in a DLT expectation for the report table. | | B. Define a function that performs a left outer join on validation_copy and report and report,
and check against the result in a DLT expectation for the report table | | C. Define a temporary table that perform a left outer join on validation_copy and report, and
define an expectation that no report key values are null | | D. Define a view that performs a left outer join on validation_copy and report, and reference
this view in DLT expectations for the report table |
D. Define a view that performs a left outer join on validation_copy and report, and reference
this view in DLT expectations for the report table
Explanation:
To validate that all records from the source are included in the derived table,
creating a view that performs a left outer join between the validation_copy table and the
report table is effective. The view can highlight any discrepancies, such as null values in
the report table's key columns, indicating missing records. This view can then be
referenced in DLT (Delta Live Tables) expectations for the report table to ensure data
integrity. This approach allows for a comprehensive comparison between the source and
the derived table.
References:
Databricks Documentation on Delta Live Tables and Expectations: Delta Live
Tables Expectations
Question # 3
A Delta Lake table was created with the below query:
Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?
| | A. Nothing will occur until a COMMIT command is executed. | | B. The table will be removed from the catalog but the data will remain in storage. | | C. The table will be removed from the catalog and the data will be deleted. | | D. An error will occur because Delta Lake prevents the deletion of production data. | | E. Data will be marked as deleted but still recoverable with Time Travel. |
C. The table will be removed from the catalog and the data will be deleted.
Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog
and the data is deleted. This is because Delta Lake is a transactional storage layer that
provides ACID guarantees. When a table is dropped, the transaction log is updated to
reflect the deletion of the table and the data is deleted from the underlying storage.
References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table
Question # 4
| A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook.
Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a
serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run? | | A. Because all tasks are managed as a dependency graph, no changes will be committed
to the Lakehouse until all tasks have successfully been completed. | | B. Tasks B and C will attempt to run as configured; any changes made in task A will be
rolled back due to task failure. | | C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse;
because task A failed, all commits will be rolled back automatically. | | D. Tasks B and C will be skipped; some logic expressed in task A may have been
committed before task failure. | | E. Tasks B and C will be skipped; task A will not commit any changes because of stage
failure. |
D. Tasks B and C will be skipped; some logic expressed in task A may have been
committed before task failure.
Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are
executed in a dependency graph. If a task fails, the downstream tasks that depend on it are
skipped and marked as Upstream failed. However, the failed task may have already
committed some changes to the Lakehouse before the failure occurred, and those changes
are not rolled back automatically. Therefore, the job run may result in a partial update of the
Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to
ensure that the changes are only committed when the entire job run succeeds.
Alternatively, you can use the Run if condition to configure tasks to run even when some or
all of their dependencies have failed, allowing your job to recover from failures and
continue running. References:
transactional writes: https://docs.databricks.com/delta/deltaintro.html#transactional-writes
Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html
Question # 5
An hourly batch job is configured to ingest data files from a cloud object storage container
where each batch represent all records produced by the source system in a given hour.
The batch job to process these records into the Lakehouse is sufficiently delayed to ensure
no late-arriving data is missed. The user_id field represents a unique key for the data,
which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login
BIGINT, auto_pay BOOLEAN, last_updated BIGINT
New records are all ingested into a table named account_history which maintains a full
record of all data in the same schema as the source. The next table in the system is named
account_current and is implemented as a Type 1 table representing the most recent value
for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed
hourly, which implementation can be used to efficiently update the described
account_current table as part of each hourly batch job?
| | A. Use Auto Loader to subscribe to new files in the account history directory; configure a
Structured Streaminq trigger once job to batch update newly detected files into the account
current table. | | B. Overwrite the account current table with each batch using the results of a query against
the account history table grouping by user id and filtering for the max value of last updated. | | C. Filter records in account history using the last updated field and the most recent hour
processed, as well as the max last iogin by user id write a merge statement to update or
insert the most recent value for each user id. | | D. Use Delta Lake version history to get the difference between the latest version of
account history and one version prior, then write these records to account current. | | E. Filter records in account history using the last updated field and the most recent hour
processed, making sure to deduplicate on username; write a merge statement to update or
most recent value for each username. |
C. Filter records in account history using the last updated field and the most recent hour
processed, as well as the max last iogin by user id write a merge statement to update or
insert the most recent value for each user id.
Explanation:
This is the correct answer because it efficiently updates the account current
table with only the most recent value for each user id. The code filters records in account
history using the last updated field and the most recent hour processed, which means it will
only process the latest batch of data. It also filters by the max last login by user id, which
means it will only keep the most recent record for each user id within that batch. Then, it
writes a merge statement to update or insert the most recent value for each user id into
account current, which means it will perform an upsert operation based on the user id
column. Verified References: [Databricks Certified Data Engineer Professional], under
“Delta Lake” section; Databricks Documentation, under “Upsert into a table using merge”
section.
Question # 6
| A Delta Lake table representing metadata about content posts from users has the following
schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT,
post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered? | | A. Statistics in the Delta Log will be used to identify partitions that might Include files in the
filtered range. | | B. No file skipping will occur because the optimizer does not know the relationship between
the partition column and the longitude. | | C. The Delta Engine will use row-level statistics in the transaction log to identify the flies
that meet the filter criteria. | | D. Statistics in the Delta Log will be used to identify data files that might include records in
the filtered range. | | E. The Delta Engine will scan the parquet file footers to identify each row that meets the
filter criteria. |
D. Statistics in the Delta Log will be used to identify data files that might include records in
the filtered range.
Explanation:
This is the correct answer because it describes how data will be filtered when
a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on
a Delta Lake table that has the following schema: user_id LONG, post_text STRING,
post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE.
This table is partitioned by the date column. When a query is run on a partitioned Delta
Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might
include records in the filtered range. The statistics include information such as min and max
values for each column in each data file. By using these statistics, Delta Lake can skip
reading data files that do not match the filter condition, which can improve query
performance and reduce I/O costs.
Verified References: [Databricks Certified Data
Engineer Professional], under “Delta Lake” section; Databricks Documentation, under
“Data skipping” section.
Question # 7
| 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
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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
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Is
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There
are no prerequisites, but Databricks recommends relevant training to ensure
success.
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