Databricks-GenAI-Assoc Data Preparation Practice Question
Which TWO factors are most important when selecting a chunking strategy for text data prior to vectorization?
⚠ Common exam trap
Candidates often focus solely on the embedding model's limits while forgetting that the chunking strategy must also align with the business goal of retrieving semantically meaningful information.
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 maximum input token length of the target embedding model.
Selecting a chunking strategy requires balancing semantic context and technical limitations like context window size. If chunks are too small, they lack meaning; if too large, they exceed the limits of the embedding model and introduce irrelevant noise. Proper chunking is vital for maximizing the accuracy of RAG systems, as it determines the granularity of the information available for the retriever to present to the LLM.
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 maximum input token length of the target embedding model.
Why this is correct
Embedding models have strict limits on the number of tokens they can process in a single sequence. Exceeding this limit leads to truncation, which destroys semantic context and makes the resulting vectors inaccurate. Aligning chunk size with model token limits is a critical technical requirement for successful data preparation.
- ✗
The file format of the source documents (e.g., PDF vs. Word).
Why it's wrong here
While file formats dictate how you extract text, they are not the primary factor for chunking. Once the text is extracted as a string, the chunking strategy is determined by the embedding model's requirements and the semantic structure of the content, not the original container file's metadata.
- ✓
The desired level of semantic granularity for retrieval.
Why this is correct
Granularity determines whether the retrieved context is relevant to the user's specific query. Too much information leads to confusion, while too little results in incomplete answers. Tailoring chunk size to the semantic units of the domain is essential for high-precision retrieval and better LLM response generation in RAG.
- ✗
The total number of documents in the corpus.
Why it's wrong here
The total number of documents influences scaling, but not the chunking strategy itself. Whether you have ten documents or ten million, the chunking strategy should be based on the model's capabilities and the nature of the information. Focusing on total count is a distraction from the technical requirements.
- ✗
The storage cost of the resulting vector database.
Why it's wrong here
While smaller chunks lead to more vectors, storage costs are rarely the primary driver of a chunking strategy. The primary goal is retrieval accuracy, not storage optimization. Prioritizing cost over accuracy leads to suboptimal RAG performance, making this an incorrect focus for data preparation strategy.
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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.