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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Answer: C

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints : https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions : https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 153
A junior developer complains that the code in their notebook isn ' t producing the correct results in the development environment. A shared screenshot reveals that while they ' re using a notebook versioned with Databricks Repos, they ' re using a personal branch that contains old logic. The desired branch named dev-
2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
D. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-
2.3.9
E. Merge all changes back to the main branch in the remote Git repository and clone the repo again

Answer: A

Explanation:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-
2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.

NEW QUESTION # 154
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
C. When tables are created, make sure that the external keyword is used in the create table statement.
D. Whenever a database is being created, make sure that the location keyword is used
E. Whenever a table is being created, make sure that the location keyword is used.

Answer: E

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 155
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.
E. Records that violate the expectation cause the job to fail.

Answer: A

Explanation:
Explanation
The answer is, Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

NEW QUESTION # 156
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A.
B.
C. preds.write.format("delta").save("/preds/churn_preds")
D.
E. preds.write.mode("append").saveAsTable("churn_preds")

Answer: A

Explanation:
Explanation
This is the correct answer because it will save the predictions to a Delta Lake table with the ability to compare all predictions across time. The code uses the mergeInto method to perform an upsert operation, which means it will insert new records or update existing records based on the customer_id and date columns. This way, the table will always contain the latest predictions for each customer and date, and also keep the history of previous predictions. The code also uses a new job cluster to run the job, which will minimize the compute costs as it will be created and terminated for each run. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 157
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