A company is migrating their Spark workloads to a shared, centrally-managed cluster. They want to reduce the administrative overhead of maintaining multiple client environments. Which benefit of Spark Connect directly supports this goal?

Clients can submit jobs without any authentication.

Clients can run any Spark version regardless of the server version.

Clients can bypass the cluster manager and run jobs directly on the worker nodes.

Clients only need a lightweight library, so there is no need to install and maintain a full Spark distribution on each user's machine.

A company is planning to upgrade their Spark cluster to a new version. They have many users with different client environments. They want to minimize the impact of the upgrade on users. How does Spark Connect help?

Users must recompile their applications against the new server version.

Users can continue using their existing clients if they are within the supported compatibility range.

Users must upgrade their clients at the same time as the server.

Users must switch to a different programming language.

A developer is using Spark Connect to run a DataFrame transformation. They notice that the transformation runs on the server, but the client does not have access to the underlying data files. How is this possible?

The client downloads the data files to its local machine before processing.

The client sends the query plan to the server, and the server reads the data from its own storage.

The client uses a separate storage API to read the data directly.

The server sends the data files to the client for processing.

A team is evaluating whether to adopt Spark Connect for their existing Spark workloads. They are concerned about the performance overhead of the network communication between client and server. What is the primary factor that determines whether this overhead is acceptable?

The size of the data being transferred between client and server.

The version of Spark on the server.

The programming language used for the client.

The number of nodes in the cluster.

A developer is using Spark Connect from a Python client. They want to run a job that requires a specific Python library that is not installed on the Spark server. What is the best way to handle this?

Install the library on the Spark server's Python environment.

Run the job locally on the client and send the results to the server.

Use a Python UDF that includes the library code.

Use a different programming language that has the library built-in.

DatabricksCertified Associate Developer for Apache Spark

Domain 6Objective 1

Describe the Features of Spark Connect. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Part of the Using Spark Connect to deploy applications domain, which accounts for 5% of the ASSOCIATE-DEVELOPER-APACHE-SPARK exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~2–3 in this domain), expect 1–2 from this objective — we provide 19 practice questions to prepare you well beyond it. (estimate)

19questions here

4free pages

4concepts

5%of the exam

Questions 1–5

1application · medium
A company is migrating their Spark workloads to a shared, centrally-managed cluster. They want to reduce the administrative overhead of maintaining multiple client environments. Which benefit of Spark Connect directly supports this goal?
Select an answer first
2application · medium
A company is planning to upgrade their Spark cluster to a new version. They have many users with different client environments. They want to minimize the impact of the upgrade on users. How does Spark Connect help?
Select an answer first
3application · medium
A developer is using Spark Connect to run a DataFrame transformation. They notice that the transformation runs on the server, but the client does not have access to the underlying data files. How is this possible?
Select an answer first
4application · medium
A team is evaluating whether to adopt Spark Connect for their existing Spark workloads. They are concerned about the performance overhead of the network communication between client and server. What is the primary factor that determines whether this overhead is acceptable?
Select an answer first
5expert · hard
A developer is using Spark Connect from a Python client. They want to run a job that requires a specific Python library that is not installed on the Spark server. What is the best way to handle this?
Select an answer first

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