Question # 1
The business intelligence team has a dashboard configured to track various summary
metrics for retail stories. 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?
| | A. Use the Delta Cache to persists the products_per_order table in memory to quickly the
dashboard with each query. | | B. Populate the dashboard by configuring a nightly batch job to save the required to quickly
update the dashboard with each query. | | C. Use Structure Streaming to configure a live dashboard against the products_per_order
table within a Databricks notebook. | | D. Define a view against the products_per_order table and define the dashboard against
this view. |
D. Define a view against the products_per_order table and define the dashboard against
this view.
Explanation:
Given the requirement for daily refresh of data and the need to ensure quick
response times for interactive queries while controlling costs, a nightly batch job to precompute and save the required summary metrics is the most suitable approach.
By pre-aggregating data during off-peak hours, the dashboard can serve queries
quickly without requiring on-the-fly computation, which can be resource-intensive
and slow, especially with many users.
This approach also limits the cost by avoiding continuous computation throughout
the day and instead leverages a batch process that efficiently computes and stores
the necessary data.
The other options (A, C, D) either do not address the cost and performance
requirements effectively or are not suitable for the use case of less frequent data
refresh and high interactivity.
References:
Databricks Documentation on Batch Processing: Databricks Batch Processing
Data Lakehouse Patterns: Data Lakehouse Best Practices
Question # 2
| What statement is true regarding the retention of job run history? | | A. It is retained until you export or delete job run logs | | B. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3 | | C. it is retained for 60 days, during which you can export notebook run results to HTML | | D. It is retained for 60 days, after which logs are archived | | E. It is retained for 90 days or until the run-id is re-used through custom run configuration |
C. it is retained for 60 days, during which you can export notebook run results to HTML
Question # 3
| Each configuration below is identical to the extent that each cluster has 400 GB total of
RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster
configurations will result in maximum performance? | A. • Total VMs; 1
• 400 GB per Executor
• 160 Cores / Executor
| B. • Total VMs: 8
• 50 GB per Executor
• 20 Cores / Executor
| C. • Total VMs: 4
• 100 GB per Executor
• 40 Cores/Executor
| D. • Total VMs:2
• 200 GB per Executor
• 80 Cores / Executor
|
B. • Total VMs: 8
• 50 GB per Executor
• 20 Cores / Executor
Explanation:
This is the correct answer because it is the cluster configuration that will
result in maximum performance for a job with at least one wide transformation. A wide
transformation is a type of transformation that requires shuffling data across partitions,
such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming,
especially if there are too many or too few partitions. Therefore, it is important to choose a
cluster configuration that can balance the trade-off between parallelism and network
overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor
will create 8 partitions, each with enough memory and CPU resources to handle the
shuffling efficiently. Having fewer VMs with more memory and cores per executor will
create fewer partitions, which will reduce parallelism and increase the size of each shuffle
block. Having more VMs with less memory and cores per executor will create more
partitions, which will increase parallelism but also increase the network overhead and the
number of shuffle files. Verified References: [Databricks Certified Data Engineer
Professional], under “Performance Tuning” section; Databricks Documentation, under
“Cluster configurations” section.
Question # 4
| 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
Question # 5
| When scheduling Structured Streaming jobs for production, which configuration
automatically recovers from query failures and keeps costs low? | A. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
| B. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
| C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
| D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
| E. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
|
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
Explanation:
The configuration that automatically recovers from query failures and keeps
costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent
runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This
means that the cluster resources are only used when the job is running, and no
idle costs are incurred. This also ensures that the cluster is always in a clean state
and has the latest configuration and libraries for the job1.
Setting retries to unlimited means that the job will automatically restart the query in
case of any failure, such as network issues, node failures, or transient errors. This
improves the reliability and availability of the streaming job, and avoids data loss or
inconsistency2.
Setting maximum concurrent runs to 1 means that only one instance of the job can
run at a time. This prevents multiple queries from competing for the same
resources or writing to the same output location, which can cause performance
degradation or data corruption3.
Therefore, this configuration is the best practice for scheduling Structured Streaming jobs
for production, as it ensures that the job is resilient, efficient, and consistent.
References: Job clusters, Job retries, Maximum concurrent runs
Question # 6
| A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is
being stored in a bronze table, and includes the Kafka_generated timesamp, key, and
value. Three months after the pipeline is deployed the data engineering team has noticed
some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the
current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The
team plans to use the additional metadata fields to diagnose the transient processing
delays:
Which limitation will the team face while diagnosing this problem? | | A. New fields not be computed for historic records. | | B. Updating the table schema will invalidate the Delta transaction log metadata. | | C. Updating the table schema requires a default value provided for each file added. | | D. Spark cannot capture the topic partition fields from the kafka source. |
A. New fields not be computed for historic records.
Explanation:
When adding new fields to a Delta table's schema, these fields will not be
retrospectively applied to historical records that were ingested before the schema change.
Consequently, while the team can use the new metadata fields to investigate transient
processing delays moving forward, they will be unable to apply this diagnostic approach to
past data that lacks these fields.
References:
Databricks documentation on Delta Lake schema management:
https://docs.databricks.com/delta/delta-batch.html#schema-management
Question # 7
| The data architect has decided that once data has been ingested from external sources
into the
Databricks Lakehouse, table access controls will be leveraged to manage permissions for
all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production
database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that
these users are not workspace administrators, which statement describes their privileges? | | A. Group members have full permissions on the prod database and can also assign
permissions to other users or groups. | | B. Group members are able to list all tables in the prod database but are not able to see
the results of any queries on those tables. | | C. Group members are able to query and modify all tables and views in the prod database,
but cannot create new tables or views. | | D. Group members are able to query all tables and views in the prod database, but cannot
create or edit anything in the database. | | E. Group members are able to create, query, and modify all tables and views in the prod
database, but cannot define custom functions. |
D. Group members are able to query all tables and views in the prod database, but cannot
create or edit anything in the database.
Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng
group the permission to use the prod database, which means they can list and access the
tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng
command grants the eng group the permission to select data from the tables and views in
the prod database, which means they can query the data using SQL or DataFrame API.
However, these commands do not grant the eng group any other permissions, such as
creating, modifying, or deleting tables and views, or defining custom functions. Therefore,
the eng group members are able to query all tables and views in the prod database, but
cannot create or edit anything in the database.
References:
Grant privileges on a database:
https://docs.databricks.com/en/security/authauthz/table-acls/grant-privileges-database.html
Privileges you can grant on Hive metastore objects:
https://docs.databricks.com/en/security/auth-authz/table-acls/privileges.html
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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.
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The
exam covers data ingestion, processing, analytics, and visualization using Databricks,
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The
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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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