Databricks-ML-Pro Model Development Practice Question
An ML engineer wants to ensure that their model training pipeline is robust against data quality issues. Which approach, if integrated into the pipeline, most effectively detects skewed or missing values before training begins?
⚠ Common exam trap
Candidates often suggest post-training model monitoring tools for pre-training data issues, failing to recognize that DLT Expectations are designed specifically for proactive data validation during the ETL pipeline phase.
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
✓
Using DLT Expectations to define and enforce constraints on input data.
Integrating data quality checks, such as those provided by Delta Live Tables (DLT) expectations or Great Expectations, is crucial for MLOps. By enforcing schema and statistical constraints at the ingest and preparation stages, the pipeline can fail early if data quality falls below standards, preventing the training of 'garbage-in-garbage-out' models and saving compute costs while maintaining model reliability in production.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually inspecting the first 100 rows of the dataset using a notebook cell.
Why it's wrong here
Manual inspection is highly subjective, non-scalable, and cannot catch issues that only appear across large, distributed datasets. It does not provide the automated gatekeeping required to maintain pipeline integrity, meaning significant data issues could pass through and degrade the model without the developer noticing during the manual check.
- ✓
Using DLT Expectations to define and enforce constraints on input data.
Why this is correct
DLT Expectations provide a declarative way to define data quality rules. By automatically monitoring data against these rules and failing the pipeline or isolating bad records, engineers ensure that only high-quality data enters the training set, which is essential for building reliable, production-grade machine learning systems.
- ✗
Relying on the model to handle missing values by using imputation during training.
Why it's wrong here
Imputation hides data quality issues rather than addressing their root cause. If missing values are due to upstream pipeline errors, ignoring them during training may lead to biased models that perform poorly in production. Quality checks should identify the underlying data problems before they reach the modeling phase.
- ✗
Increasing the size of the training cluster to process more data.
Why it's wrong here
Scaling the cluster size does nothing to improve data quality. In fact, training on a larger cluster with poor-quality data only increases the cost and time spent producing an inferior model. Data quality must be addressed through explicit validation gates, not by throwing more computational resources at the problem.
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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-Pro 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-Pro exam.