Databricks-GenAI-Assoc Data Preparation Practice Question
You are preparing a large dataset for fine-tuning a model using Databricks Delta Live Tables (DLT). Which configuration is best for ensuring data quality and lineage in this pipeline?
⚠ Common exam trap
Candidates often overlook 'Expectations' as a quality tool, viewing them as optional. In a GenAI pipeline, they are essential for ensuring that only high-quality, validated data enters the model training.
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
✓
Implement DLT Expectations to flag or drop invalid records.
Delta Live Tables (DLT) with Expectations allows you to define constraints on data quality. By tagging data that fails these constraints, engineers ensure only clean data reaches the final training table. This provides built-in lineage and automatic quality monitoring. In the context of GenAI, clean data is paramount, as noise in the training set leads to poor model performance and unpredictable behavior in downstream applications.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use standard Spark SQL jobs without DLT.
Why it's wrong here
Standard Spark jobs do not have built-in declarative quality constraints or the same level of automated lineage tracking as DLT. While powerful, they require additional manual code to track quality and lineage, which increases the likelihood of human error and makes maintenance more difficult in complex production environments.
- ✓
Implement DLT Expectations to flag or drop invalid records.
Why this is correct
Expectations provide a declarative way to enforce quality in DLT pipelines. They ensure that the training data meets specific criteria, such as length or content validity. This reduces noise in the model training process, leading to better results and more reliable behavior, which is essential for enterprise GenAI deployments.
- ✗
Write all data to a temporary folder and clean it after training.
Why it's wrong here
Cleaning after training is inefficient and negates the benefits of data preparation. It also makes it impossible to know if the model was trained on bad data, leading to a 'garbage-in, garbage-out' scenario. Proactive, pipeline-level cleaning is the standard approach for responsible AI development in Databricks.
- ✗
Rely on the LLM to automatically filter out low-quality data during training.
Why it's wrong here
LLMs cannot reliably identify and filter out poor-quality training data during the fine-tuning process. Relying on this is not a viable strategy and will lead to model degradation. Data must be cleaned systematically before it is used for training to ensure the model learns from reliable, high-quality sources.
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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-GenAI-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-GenAI-Assoc exam.