Databricks-DA-Assoc Developing AI/BI Genie Spaces Practice Question
Exhibit
{"model": "databricks-dbr-14-x", "max_tokens": 500, "temperature": 0.2}Refer to the exhibit. The Genie Space output is consistently truncated. What is the most likely cause?
⚠ Common exam trap
Candidates often blame network latency or database timeout settings when outputs are cut off, missing the token generation threshold constraint in the configuration.
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_tokens' limit is too low for the complexity of the output.
The 'max_tokens' parameter is set to 500, which is relatively small for complex SQL generation tasks. If the generated query is long, or if the model provides a detailed explanation along with the SQL, it will hit this limit and truncate the response. Increasing the 'max_tokens' limit will allow the model to provide complete responses, ensuring that the SQL is valid and the context provided is not cut off mid-sentence.
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, causing the model to finish early.
Why it's wrong here
Low temperature causes the model to be more deterministic, not to finish early. Truncation is almost always related to token limits or output length constraints. A low temperature setting is actually beneficial for generating consistent SQL, so it should not be the cause of the observed truncation behavior.
- ✓
The 'max_tokens' limit is too low for the complexity of the output.
Why this is correct
The 'max_tokens' parameter restricts the total length of the model's response. When this limit is reached, the model simply stops, leading to truncated output. For complex analytical queries that involve joins, aggregations, and explanations, 500 tokens is often insufficient, necessitating an increase in the limit to ensure complete outputs.
- ✗
The model version is incompatible with the SQL Warehouse.
Why it's wrong here
Model version incompatibility would typically result in a complete failure or a clear error message, not truncated text. Truncation is a symptom of length constraints. If the model were incompatible, it would likely fail to produce any output at all or throw an exception during the initialization phase.
- ✗
The SQL Warehouse is timing out during the query generation.
Why it's wrong here
Warehouse timeouts result in an error or a hanging request, not a truncated text string from the LLM. Truncation is an artifact of the model generation process itself, governed by the token settings in the Genie configuration, which is independent of the warehouse execution performance or its status.
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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
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