Databricks-GenAI-Assoc Data Preparation Practice Question
When preparing a dataset for fine-tuning an LLM, you need to ensure the data is representative of the target domain. What is the most effective approach to detect and mitigate sampling bias in your training set using Databricks?
⚠ Common exam trap
Candidates tend to focus exclusively on model hyperparameter tuning while ignoring raw dataset distributions, missing structural imbalances present in the training inputs.
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
✓
Perform statistical profiling on feature distributions and re-sample if necessary.
Using exploratory data analysis (EDA) with Databricks SQL or Spark to analyze distribution statistics is the most effective way to identify bias. By comparing the distribution of the training set to a representative sample of real-world production data, engineers can identify under-represented categories. This ensures that the fine-tuned model performs reliably across all expected input scenarios, preventing the model from developing blind spots.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the batch size during the fine-tuning process.
Why it's wrong here
Batch size affects training convergence and hardware utilization but has no impact on data sampling bias. Bias is a structural issue within the dataset itself, not a training parameter. Adjusting the batch size will not correct the underlying lack of representativeness in the training data provided.
- ✓
Perform statistical profiling on feature distributions and re-sample if necessary.
Why this is correct
Statistical profiling reveals imbalances, allowing engineers to apply techniques like oversampling or undersampling to correct them. This quantitative approach is objective and standard for ensuring high-quality machine learning training sets. It prevents the model from favoring specific patterns simply because they were more frequent in the dataset.
- ✗
Change the model architecture to a larger parameter count.
Why it's wrong here
Increasing model size might allow the model to memorize more data, but it does not address the fundamental issue of sampling bias. If the training data is inherently biased, a larger model will simply learn those biases more effectively, potentially leading to worse outcomes in production scenarios.
- ✗
Use a random seed to shuffle the data before splitting.
Why it's wrong here
Shuffling helps with training stability but does not resolve structural sampling bias. If the entire dataset is skewed, shuffling only ensures that the skew is evenly distributed across training and validation splits. It does not introduce the missing data or diversity required to mitigate the actual bias.
About these practice questions
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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.