Databricks-GenAI-Assoc Design Applications Practice Question
Exhibit
{
"model": "mistral-7b",
"parameters": {
"max_new_tokens": 512,
"temperature": 0.1,
"top_p": 0.9
}
}Refer to the exhibit. An engineer is testing a model endpoint. The outputs are too brief and often stop mid-sentence. What is the most likely cause?
⚠ Common exam trap
Candidates often blame the model's training or temperature settings for truncated output, failing to recognize that max_new_tokens is a hard configuration limit that forces an early stop.
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
✓
The max_new_tokens limit is too low for the expected response length.
The 'max_new_tokens' parameter is set to 512, which imposes a hard limit on the length of the generated output. If the model needs more tokens to complete its thought, it will be abruptly cut off. Increasing this limit is the logical troubleshooting step when encountering truncation issues in generative model responses, ensuring the model has sufficient space to finalize its output properly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The temperature is too low for the model to generate full sentences.
Why it's wrong here
Temperature controls the randomness of the token selection, not the length of the output. While a very low temperature can make the model repetitive, it does not cause it to truncate its response. The truncation is directly tied to the token limit constraint provided in the configuration.
- ✗
The top_p parameter is too high, leading to early termination.
Why it's wrong here
Top-p sampling controls the diversity of the generated tokens by considering only the top probability mass. It has no effect on the maximum number of tokens allowed. Truncation mid-sentence is a hallmark of reaching the maximum token limit, not a result of token selection sampling strategies.
- ✓
The max_new_tokens limit is too low for the expected response length.
Why this is correct
The 'max_new_tokens' parameter restricts the number of tokens the model is permitted to generate. If the model's intended response exceeds this count, it stops generation. This configuration is the direct cause of the truncation observed, and it must be increased to accommodate longer, more complete responses.
- ✗
The model version is incompatible with the specified parameters.
Why it's wrong here
These parameters are standard across most generative models served on Databricks. While individual models might behave differently, the truncation behavior is consistent with hitting a hard token limit. There is no evidence in the exhibit to suggest that these parameters are incompatible with the Mistral-7B 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-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.