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Total 247 Questions | Updated On: Nov 18, 2024

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Question 1

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 dev2.3.9 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 make a pull request use the Databricks REST API to update the current branch to dev2.3.9

B : Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch

C : Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch

D : Merge all changes back to the main branch in the remote Git repository and clone the repo again

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

Answer: B

Question 2

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data. Which statement is a possible explanation for this behavior?

A : %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time

B : Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

C : %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

D : Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala

E : %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Answer: C

Question 3

The data engineering team has a Silver table called ‘sales_cleaned’ where new sales data is appended in near real-time.

They want to create a new Gold-layer entity against the ‘sales_cleaned’ table to calculate the year-to-date (YTD) of the sales amount. The new entity will have the following schema:

country_code STRING, category STRING, ytd_total_sales FLOAT, updated TIMESTAMP

It’s enough for these metrics to be recalculated once daily. But since they will be queried very frequently by several business teams, the data engineering team wants to cut down the potential costs and latency associated with materializing the results.

Which of the following solutions meets these requirements?

A : Define the new entity as a view to avoid persisting the results each time the metrics are recalculated

B : Define the new entity as a global temporary view since it can be shared between notebooks or jobs that share computing resources.

C : Configuring a nightly batch job to recalculate the metrics and store them as a table overwritten with each update

D : Create multiple tables, one per business team so the metrics can be queried quickly and efficiently.

E : All the above solutions meet the required requirements since Databricks uses the Delta Caching feature

Answer: C

Question 4

The following code has been migrated to a Databricks notebook from a legacy workload:

A : %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time

B : Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

C : %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

D : Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala

E : %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.

Answer: C

Question 5

The Databricks workspace administrator has configured interactive clusters for each of the data engineering

groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to

execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following

describes the minimal permissions a user would need to start and attach to an already configured cluster.

A : "Can Manage" privileges on the required cluster

B : Workspace Admin privileges, cluster creation allowed, "Can Attach To" privileges on the required cluster

C : Cluster creation allowed, "Can Attach To" privileges on the required cluster

D : "Can Restart" privileges on the required cluster

E : Cluster creation allowed, "Can Restart" privileges on the required cluster

Answer: A

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Total 247 Questions | Updated On: Nov 18, 2024

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