Which Python variable contains a list of directories to be searched when trying to locate required modules?

1. A. importlib.resource path
2. B. ,sys.path
3. C. os-path
4. D. pypi.path
5. E. pylib.source

Correct Answer: B

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?

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

Correct Answer: B
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.

A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

1. A. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
2. B. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
3. C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
4. D. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Correct Answer: A
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.References:
✑ Databricks Documentation on Delta Lake: Delta Lake Guide
✑ Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

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?

1. A. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
2. B. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
3. C. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
4. D. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
5. E. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Correct Answer: D
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.

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?

1. 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.
2. 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.
3. 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.
4. D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
5. E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

Correct Answer: D
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/delta- intro.html#transactional-writes
✑ Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html