Databricks-Spark-Assoc Using Spark Connect Practice Question
A developer is troubleshooting a Spark Connect client that intermittently fails to create a session against a Databricks cluster. The cluster is configured to auto-terminate after 20 minutes of inactivity. The client script runs on a schedule every hour. What is the most likely cause of the intermittent session creation failures?
⚠ Common exam trap
The trap here is blaming credentials or library versions for intermittent failures when the cluster lifecycle is the variable that matches the schedule.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The auto-terminated cluster is not running when the hourly script attempts to connect, so the session cannot be established until the cluster is started.
Spark Connect needs a live server to accept the connection. A cluster that auto-terminates after 20 minutes will be stopped when an hourly job runs, so the client cannot establish a session unless it triggers a start or the job is configured to start the cluster. The schedule and idle timeout together explain the intermittent failures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The auto-terminated cluster is not running when the hourly script attempts to connect, so the session cannot be established until the cluster is started.
Why this is correct
With a 20-minute idle timeout and an hourly schedule, the cluster terminates between runs. Spark Connect requires a running cluster to accept the gRPC session. Unless the client or job is configured to start the cluster, session creation fails. This matches the intermittent, schedule-linked pattern described.
- ✗
The Spark Connect client library is incompatible with the Python version on the client machine.
Why it's wrong here
A Python version mismatch would cause consistent failures at import or session creation, not intermittent ones tied to a schedule. The scenario describes failures that correlate with idle periods, so a static compatibility issue is not the best explanation. Compatibility problems do not resolve themselves between runs.
- ✗
The personal access token expires every hour and must be regenerated before each run.
Why it's wrong here
Personal access tokens have configurable lifetimes and are not tied to hourly cycles by default. Token expiry would affect all runs after the expiry date, not alternate with cluster idle periods. The scenario points to cluster state, not credential rotation, as the variable factor.
- ✗
Spark Connect sessions cannot be created from scheduled scripts and must be created interactively.
Why it's wrong here
Spark Connect sessions are created programmatically and work fine in scheduled scripts. There is no requirement for interactive creation. The real issue in the scenario is the cluster's availability, not the nature of the scheduler invoking the client.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-Spark-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-Spark-Assoc exam.