Databricks-GenAI-Assoc Data Preparation Practice Question
You need to store embeddings generated by an LLM in a Delta table. Which data type is most efficient for storing these high-dimensional vector arrays in Databricks?
⚠ Common exam trap
Candidates often assume complex custom objects or string-serialized JSON are necessary. Delta natively supports arrays, which are significantly more efficient for vector math and similarity searches.
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
✓
Store as an array of doubles.
Storing embeddings as an 'Array' of 'Floats' (or 'Doubles') in a Delta table is the most efficient and native way to handle them. This allows Spark to utilize vectorized operations for similarity search calculations and enables compatibility with various indexing libraries. Using this approach ensures high performance during retrieval, minimizing the latency of finding the most relevant context for the LLM during generation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store as a Base64 encoded string.
Why it's wrong here
Encoding vectors as strings is highly inefficient. It requires decoding before any similarity comparison can take place, adding significant latency and CPU overhead to the retrieval process. It also prevents the use of optimized vector similarity search libraries that expect numerical arrays, making this a very poor storage strategy.
- ✓
Store as an array of doubles.
Why this is correct
An array of doubles is the standard format for vector embeddings in Delta Lake. It is natively supported, memory-efficient, and easily accessible by Spark's vectorized query engine and popular libraries like FAISS or MosaicML Vector Search. This format provides the best balance between storage performance and computational efficiency for retrieval.
- ✗
Store as separate columns for each dimension (e.g., dim1, dim2, ...).
Why it's wrong here
Creating thousands of columns for a high-dimensional vector is extremely slow and causes severe performance degradation in Spark. It also makes schema management impossible. This approach is fundamentally incompatible with the way modern data platforms handle high-dimensional machine learning data and should be avoided at all costs.
- ✗
Store as a compressed binary blob (e.g., serialized byte array).
Why it's wrong here
While space-efficient, binary blobs are not natively searchable in Spark. To use them, you must deserialize the data for every single comparison, which negates the performance benefit. The overhead of constant serialization and deserialization makes this inappropriate for production RAG systems that require low-latency responses.
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Last reviewed September 2026 · checked against the official Databricks exam blueprint
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