Databricks-GenAI-Assoc Design Applications Practice Question
When designing an application that requires fine-tuning a small model (like Llama-3-8B) on Databricks, which THREE factors must be considered to ensure a successful training job?
⚠ Common exam trap
Candidates sometimes forget hyperparameter tuning specifics, ignoring learning rate configuration which directly causes catastrophic forgetting during fine-tuning.
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
✓
Ensuring the training data is sufficiently representative of the target task.
Training small models on Databricks requires careful consideration of compute, data quality, and hyperparameter tuning. By ensuring sufficient GPU memory, clean data, and properly tuned learning rates, engineers can successfully adapt the model to new domains. These factors are critical to avoid common pitfalls like catastrophic forgetting or overfitting, which can render the fine-tuned model less useful than its base version.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensuring the training data is sufficiently representative of the target task.
Why this is correct
Data quality and representativeness are the most critical factors for fine-tuning success. If the training data does not cover the breadth of scenarios the model will encounter, the fine-tuned model will fail to generalize. Ensuring the dataset is high-quality and well-curated is the first step toward effective model adaptation.
- ✓
Selecting a GPU-accelerated instance type sufficient for the model's memory.
Why this is correct
Fine-tuning requires loading the model and its optimizer states into GPU memory. Choosing an instance type with insufficient VRAM will result in out-of-memory errors and failed jobs. Proper hardware selection is essential to handle the memory-intensive nature of training, ensuring the job completes successfully and efficiently.
- ✗
Disabling checkpointing to save storage costs.
Why it's wrong here
Checkpointing is vital for long training jobs to allow for recovery in case of failures. Disabling it is poor practice because it risks losing hours of compute time if a node fails or a transient error occurs. Reliability in distributed training workflows is far more valuable than marginal storage savings.
- ✓
Configuring appropriate learning rates to prevent catastrophic forgetting.
Why this is correct
Learning rates control the magnitude of weight updates during training. If set too high, the model may overwrite its previous knowledge, leading to catastrophic forgetting. Tuning the learning rate is a critical hyperparameter optimization task that ensures the model retains its language abilities while learning the new domain knowledge.
- ✗
Increasing the batch size to the maximum possible for all datasets.
Why it's wrong here
While large batch sizes can improve training speed, they are constrained by available GPU memory. Attempting to force the maximum batch size often leads to out-of-memory errors. The batch size must be balanced with the available hardware resources to ensure stable and successful model training within the environment.
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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
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