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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Modeling and Storage	- Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies
Topic 2: Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks
Topic 3: Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Topic 4: Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q202-Q207):

NEW QUESTION # 202
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 ofVACUUMcommands 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 default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
B. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
C. 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.
D. 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.
E. 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.

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 203
You are working to set up two notebooks to run on a schedule, the second notebook is dependent on the first notebook but both notebooks need different types of compute to run in an optimal fashion, what is the best way to set up these notebooks as jobs?

A. Each task can use different cluster, add these two notebooks as two tasks in a single job with linear dependency and modify the cluster as needed for each of the tasks
B. A Job can only use single cluster, setup job for each notebook and use job dependency to link both jobs together
C. Use a very large cluster to run both the tasks in a single job
D. Use DELTA LIVE PIPELINES instead of notebook tasks
E. Use a single job to setup both notebooks as individual tasks, but use the cluster API to setup the second cluster before the start of second task

Answer: A

Explanation:
Explanation
Tasks in Jobs support different clusters for each task in the same job.

NEW QUESTION # 204
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the fieldpk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
E. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.

Answer: B

Explanation:
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronzetable, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified References: [Databricks Certified Data Engineer Professional], under
"Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 205
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The update throws an error because changes to existing columns in the target schema are not supported.
C. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

Answer: C

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
Reference:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.html#schema-evolution

NEW QUESTION # 206
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company.
A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users.
Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. ''Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
C. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
D. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.

Answer: C

Explanation:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.
:
Databricks Documentation on Secret Management: Secrets

NEW QUESTION # 207
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