Databricks-ML-Assoc ML Workflows Practice Question
An ML engineer is using Databricks Jobs to orchestrate a multi-step ML pipeline. The pipeline includes a task that trains a model and logs it to MLflow, followed by a task that registers the model in the Model Registry. The engineer wants to ensure that the model is only registered if its accuracy exceeds a threshold. Which approach best implements this conditional logic?
⚠ Common exam trap
The trap here is assuming that task dependencies or Model Registry settings can enforce metric-based conditions, but they only handle success/failure or metadata, not dynamic thresholds.
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
✓
In the training task, evaluate the model and if accuracy exceeds the threshold, call the MLflow Model Registry API to register the model.
The most straightforward way to conditionally register a model based on accuracy is to perform the evaluation within the training task and call the Model Registry API only if the threshold is met. This keeps the logic in one place and avoids unnecessary task dependencies. Other approaches either do not enforce the metric condition or require additional complexity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the Model Registry to automatically register models only if they meet a performance threshold set in the model's metadata.
Why it's wrong here
MLflow Model Registry does not have built-in automatic registration based on performance thresholds. Registration is a manual or programmatic step. Setting metadata does not trigger registration. Therefore, this approach is not feasible without external logic to evaluate and register the model.
- ✓
In the training task, evaluate the model and if accuracy exceeds the threshold, call the MLflow Model Registry API to register the model.
Why this is correct
Embedding the evaluation and conditional registration within the training task allows the logic to check accuracy and only register if the threshold is met. This is a common pattern in Databricks Jobs, where a single task can contain multiple steps. It ensures that registration is gated by the metric, avoiding unnecessary separate tasks.
- ✗
Use a Databricks Job with a conditional task that checks a metric value stored in a Delta table and then triggers registration.
Why it's wrong here
While Databricks Jobs support conditional tasks via the 'condition_task' type, they evaluate conditions based on task values or job parameters, not directly on metrics stored in a Delta table. You would need to expose the metric as a task value. This approach adds complexity and is not the most direct way to implement the logic.
- ✗
Use a Databricks Job with multiple tasks and define a dependency that runs the registration task only if the training task succeeds.
Why it's wrong here
Defining a dependency based on task success only checks if the training task completed without error, not if the model's accuracy meets a threshold. The training task could succeed but produce a low-accuracy model. Thus, this approach does not enforce the accuracy condition and could register a subpar model.
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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-ML-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-ML-Assoc exam.