Databricks-GenAI-Assoc Design Applications Practice Question
A GenAI engineer is designing a retrieval-augmented generation application whose source documents are long PDFs. Early testing shows that answers are vague because retrieved chunks contain several unrelated topics, and the language model frequently cites content that does not support its claims. The engineer wants to improve chunk quality before indexing. Which TWO changes should the engineer make to the ingestion pipeline? (Choose two.)
⚠ Common exam trap
The trap here is treating vague answers as a model-tuning problem, when the evidence points to ingestion-stage chunking that mixes topics and drops boundary sentences.
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
✓
Split documents along semantic boundaries such as sections and headings instead of a fixed character count
Chunk quality is determined at ingestion: splitting on semantic boundaries keeps each chunk topically focused, and overlap preserves meaning across boundaries. Together they raise the precision of retrieved context so the model has grounded material to cite. Embedding dimension, temperature, and oversized single chunks do not correct incoherent chunking.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Split documents along semantic boundaries such as sections and headings instead of a fixed character count
Why this is correct
Semantic splitting keeps each chunk focused on a single topic, so retrieved context is coherent and the language model is less likely to blend unrelated statements. Fixed-size splitting is what produced multi-topic chunks in this scenario, and respecting document structure directly addresses that root cause.
- ✗
Store the full PDF text in a single chunk per document
Why it's wrong here
One chunk per document maximizes topic mixing and dilutes the embedding across the entire document, making similarity search less discriminating. Large chunks also consume prompt budget and bury the relevant passage among irrelevant text, worsening the exact symptoms the engineer is trying to fix.
- ✗
Increase the embedding dimension of the model used for the index
Why it's wrong here
Embedding dimension affects representational capacity, not the internal coherence of a chunk. Multi-topic chunks would still retrieve poorly because their vectors average several themes, so a higher-dimensional model does not fix the underlying chunking problem and adds storage and query cost.
- ✗
Lower the temperature setting on the language model
Why it's wrong here
Temperature influences sampling randomness in generation, not what context is retrieved or how well a chunk supports a claim. Vague answers driven by poor chunk boundaries persist regardless of temperature, so this change does not address the ingestion-stage defect.
- ✓
Add overlapping context between adjacent chunks
Why this is correct
Overlap preserves sentences that straddle a boundary, preventing the loss of meaning when a key statement spans two chunks. Combined with semantic boundaries, overlap reduces the chance that a retrieved chunk omits the very sentence needed to support an answer, which improves grounding.
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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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