AI0-001 AI Infrastructure and Technologies Practice Question
A retail analytics team is choosing a vector database to power semantic search over millions of product descriptions and support retrieval-augmented generation. The team must keep infrastructure costs predictable and needs fast approximate nearest neighbor queries as the index grows. Which TWO characteristics of approximate nearest neighbor indexing should the team evaluate when selecting the vector database? (Choose two.)
⚠ Common exam trap
The trap here is conflating database conveniences such as ACID guarantees or built-in embedding with the index characteristics that actually control ANN recall, latency, and memory cost.
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 recall-versus-latency tradeoff controlled by index search parameters
ANN indexes are evaluated mainly on the recall-versus-latency curve and on how much memory the index needs to stay resident. Those two factors determine whether semantic search returns relevant products quickly and whether the monthly infrastructure bill stays predictable as the catalog grows. The other listed traits concern transaction semantics, column typing, or embedding generation, none of which govern ANN index performance.
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 recall-versus-latency tradeoff controlled by index search parameters
Why this is correct
ANN indexes such as HNSW expose search-time parameters, for example efSearch, that trade recall against query latency. At millions of vectors, raising the parameter improves the chance of returning true nearest neighbors but increases per-query work and cost. The team must measure this curve on its own data to pick a setting that meets both relevance expectations and predictable infrastructure spend.
- ✗
Whether the database enforces ACID transactions across all vector writes
Why it's wrong here
Full ACID guarantees are not what makes ANN search fast or cost-predictable. Vector stores typically offer eventual consistency or per-record atomicity, and demanding serializable transactions across the whole index would add coordination overhead without improving nearest neighbor quality. This characteristic is largely irrelevant to the stated latency and cost constraints.
- ✗
Whether the database can generate the embeddings from raw text itself
Why it's wrong here
Embedding generation is normally performed by a separate model or an integrated pipeline before vectors are written. Whether the database embeds text internally is a convenience feature, not a property of ANN indexing, and it does not determine query recall or memory cost. It is therefore not one of the indexing characteristics the team should weigh here.
- ✗
The ability to store product descriptions as fixed-width CHAR columns
Why it's wrong here
Fixed-width character columns are a relational modeling detail and have no bearing on ANN query performance. Semantic search depends on embedding vectors and the index structure built over them, not on how the source text is typed in a table. Selecting a vector database on this basis would not address recall, latency, or cost.
- ✓
The memory footprint required to hold the index resident for fast queries
Why this is correct
Graph-based ANN indexes keep adjacency structures in memory, and that footprint scales with vector count and dimensionality. For millions of product embeddings, RAM sizing directly determines instance class and therefore monthly cost. If the index exceeds available memory, queries fall back to disk and latency spikes, so the team must evaluate memory requirements alongside quantization options.
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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 CompTIA exam blueprint
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