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Databricks Certified Data Engineer Associate Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

A. They can configure the clusters to be single-node
B. They can use jobs clusters instead of all-purpose clusters
C. They can configure the clusters to autoscale for larger data sizes
D. They can use endpoints available in Databricks SQL
E. They can use clusters that are from a cluster pool

Answer: B

NEW QUESTION # 40
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task has the same dependency libraries as the new task
B. When another task needs to successfully complete before the new task begins
C. When another task needs to fail before the new task begins
D. When another task needs to be replaced by the new task
E. When another task needs to use as little compute resources as possible

Answer: B

Explanation:
A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other's outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
Task A: Ingest data from a source using Auto Loader
Task B: Transform the data using Spark SQL
Task C: Write the data to a Delta Lake table
Task D: Analyze the data using Spark ML
Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
Task A: No dependencies
Task B: Depends on Task A
Task C: Depends on Task B
Task D: Depends on Task C
Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
Whether the task needs to be replaced by another task
Whether the task needs to fail before another task begins
Whether the task has the same dependency libraries as another task
Whether the task needs to use as little compute resources as possible Reference: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

NEW QUESTION # 41
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

A. New lead data engineer
B. Original data engineer
C. This transfer is not possible
D. Databricks account representative
E. Workspace administrator

Answer: E

Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. References: Workspace access control, Manage Unity Catalog object ownership.

NEW QUESTION # 42
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: A

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
Reference:
Databricks documentation on Structured Streaming: Structured Streaming in Databricks

NEW QUESTION # 43
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain aggregations than Silver tables.
B. Gold tables are more likely to contain more data than Silver tables.
C. Gold tables are more likely to contain truthful data than Silver tables.
D. Gold tables are more likely to contain valuable data than Silver tables.
E. Gold tables are more likely to contain a less refined view of data than Silver tables.

Answer: A

Explanation:
According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer.
They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:
* What is the medallion lakehouse architecture?
* What is a Medallion Architecture?

NEW QUESTION # 44
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