Databricks Certified Generative AI Engineer Associate Bundle

Not the strongest implement choice for this specific question. Register the MLflow model as a named Unity Catalog model version and grant only approved use/manage rights. This action primarily relates to Unity Catalog registered model version and privileges: MLflow-logged models can be registered in Unity Catalog, with governed names, permissions and traceable version lineage. However, the described case — A deployed chatbot silently uses a different embedding model from the retrieval index built in development. — is testing RAG production bundle metadata and dependencies. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest implement choice for this specific question. Compare storage-optimized versus standard options, supported limits and actual query latency before selection. This action primarily relates to Standard versus storage-optimized Vector Search index: Index choice trades capacity, update speed, latency and storage economics; availability and feature support may vary by cloud/region. However, the described case — A deployed chatbot silently uses a different embedding model from the retrieval index built in development. — is testing RAG production bundle metadata and dependencies. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest implement choice for this specific question. Query the relevant index with a compatible query embedding, select authorized matches and pass them to the prompt. This action primarily relates to Vector Search retrieval workflow and query integration: Semantic retrieval uses an embedding model to query a compatible index, returns ranked passages and metadata for grounded generation. However, the described case — A deployed chatbot silently uses a different embedding model from the retrieval index built in development. — is testing RAG production bundle metadata and dependencies. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Run a fixed request through each stage and assert the final API schema and reference grounding. This action primarily relates to Simple prompt-retrieval-generation chain assembly: A minimal chain composes a retriever, prompt, LLM and output parser, preserving required variables and failure states. However, the described case — A chain logged under Python package version A fails to deserialize when the model serving image ships incompatible version B. — is testing Deployed model dependency drift and environment compatibility. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Exercise failed tests and confirm release is blocked, then deploy and verify a passing version. This action primarily relates to CI/CD pipelines for retrieval and agent components: Production promotions should test source index, prompt, model packaging and tool behavior independently before release. However, the described case — A chain logged under Python package version A fails to deserialize when the model serving image ships incompatible version B. — is testing Deployed model dependency drift and environment compatibility. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Validate returned IDs, relevant scores and grounded answer citations on known reference queries. This action primarily relates to Vector Search retrieval workflow and query integration: Semantic retrieval uses an embedding model to query a compatible index, returns ranked passages and metadata for grounded generation. However, the described case — A chain logged under Python package version A fails to deserialize when the model serving image ships incompatible version B. — is testing Deployed model dependency drift and environment compatibility. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect license text, model provenance, intended distribution and operational hosting constraints. This action primarily relates to Open model license versus managed endpoint obligations: Model availability on a hub does not itself confer commercial training, redistribution or hosting rights. However, the described case — A team switches from a 768-dimensional embedding model to a 1024-dimensional model while keeping the same vector index. — is testing Embedding model swap and reindex compatibility. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect training/licensing terms, model-card task benchmarks, input limits and restrictions. This action primarily relates to Model cards and task-specific limitations: A model card records intended uses, supported language/context, safety limitations and evaluation scope relevant to selection. However, the described case — A team switches from a 768-dimensional embedding model to a 1024-dimensional model while keeping the same vector index. — is testing Embedding model swap and reindex compatibility. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect which indicators are measured before deployment versus on live traffic and feedback. This action primarily relates to Offline evaluation versus ongoing production monitoring: Offline test datasets catch regressions pre-release; production monitoring detects drift, real-user failures and evolving prompts. However, the described case — A team switches from a 768-dimensional embedding model to a 1024-dimensional model while keeping the same vector index. — is testing Embedding model swap and reindex compatibility. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest implement choice for this specific question. Mask or omit unnecessary identifiers before inference and enforce approved data-handling controls. This action primarily relates to PII masking and output moderation as complementary guardrails: Input redaction and output checks serve distinct purposes; retain only necessary identifiers and validate actions separately. However, the described case — A retrieved wiki page tells the agent to email confidential documents to an external address. — is testing Prompt-injection defenses for retrieved context. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest implement choice for this specific question. Require evidence-to-claim matching and refuse or correct answers unsupported by retrieved clauses. This action primarily relates to Hallucinated facts and unsupported citations: Fluent responses can invent facts and citations; faithfulness must be checked against supplied evidence, not writing style. However, the described case — A retrieved wiki page tells the agent to email confidential documents to an external address. — is testing Prompt-injection defenses for retrieved context. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest implement choice for this specific question. Map runnable outputs to matching prompt-variable names and validate the input dictionary contract. This action primarily relates to LangChain runnable composition and invocation: Composable runnable chains pass typed values between retrieval, prompt and model stages; each binding must preserve expected input keys. However, the described case — A retrieved wiki page tells the agent to email confidential documents to an external address. — is testing Prompt-injection defenses for retrieved context. It does not directly establish the implement outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Simulate quality and latency threshold breaches and confirm responsible owners receive alerts. This action primarily relates to Production performance KPIs and incident thresholds: Monitoring should include answer correctness, retrieval coverage, safety, latency, drift and per-user harm indicators, not just availability. However, the described case — A support RAG app fails some answers, but team cannot tell whether retrieval or generation caused each error. — is testing MLflow GenAI tracing, built-in and custom scorers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Reproduce a failing request and verify a sanitized trace contains enough evidence to diagnose it. This action primarily relates to Inference logging and production trace privacy: Inference logs and tracing permit diagnosis of real issues but must be sampled, retained and redacted safely. However, the described case — A support RAG app fails some answers, but team cannot tell whether retrieval or generation caused each error. — is testing MLflow GenAI tracing, built-in and custom scorers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Re-evaluate previously disputed cases and confirm inter-rater agreement improves after calibration. This action primarily relates to Human SME feedback rubric and reviewer calibration: Domain experts should use a shared rubric, calibrate disagreements and maintain adjudicated examples to improve model evaluation. However, the described case — A support RAG app fails some answers, but team cannot tell whether retrieval or generation caused each error. — is testing MLflow GenAI tracing, built-in and custom scorers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Validate tool discovery and a permitted request for both services, including denied action testing. This action primarily relates to Managed versus external versus custom MCP server integration: MCP standardizes tool discovery; managed servers reduce hosting, external services need credentials and custom servers wrap bespoke actions. However, the described case — A product catalog has 85 million vectors with stringent p95 search latency and frequent incremental updates. — is testing Standard versus storage-optimized Vector Search index. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Load an explicitly registered version and confirm its dependencies and outputs match the approved evaluation run. This action primarily relates to Unity Catalog registered model version and privileges: MLflow-logged models can be registered in Unity Catalog, with governed names, permissions and traceable version lineage. However, the described case — A product catalog has 85 million vectors with stringent p95 search latency and frequent incremental updates. — is testing Standard versus storage-optimized Vector Search index. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Test session resume for the same user and deny cross-user recall of private ticket details. This action primarily relates to Persistent conversation memory with authorized datastore: Transient prompt context differs from persistent user state; durable memory needs explicit storage, identity scope and retention. However, the described case — A product catalog has 85 million vectors with stringent p95 search latency and frequent incremental updates. — is testing Standard versus storage-optimized Vector Search index. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest safeguard choice for this specific question. Use suitably low variability and an explicit output schema, preserving deterministic financial calculations outside LLM. This action primarily relates to Temperature and deterministic format tradeoffs: Decoding settings can affect variability and cost but cannot replace validation, grounding or application logic. However, the described case — An invoice workflow needs exact structured amounts and dates but developers choose an unconstrained chat generator only. — is testing Specialized model versus general LLM. It does not directly establish the safeguard outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest safeguard choice for this specific question. Do not cherry-pick average metrics that hide severe high-impact task failures. This action primarily relates to Model evaluation with paired benchmarks: Accuracy, faithfulness, relevance and refusal behavior require task-specific scoring, and a model switch needs common test conditions. However, the described case — An invoice workflow needs exact structured amounts and dates but developers choose an unconstrained chat generator only. — is testing Specialized model versus general LLM. It does not directly establish the safeguard outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Run reproducible pairwise experiments with confidence and segment-level result analysis. This action primarily relates to Model evaluation with paired benchmarks: Accuracy, faithfulness, relevance and refusal behavior require task-specific scoring, and a model switch needs common test conditions. However, the described case — A customer success supervisor agent must answer revenue questions from governed sales tables and draft a recommendation. — is testing Multi-agent Genie Space tool routing and governed answers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Inject adversarial retrieved passages and verify they cannot cause unauthorized actions or data disclosure. This action primarily relates to Prompt-injection defenses for retrieved context: Retrieved content is lower-trust data; embedded instructions must not trigger privileged tool calls or override assistant rules. However, the described case — A customer success supervisor agent must answer revenue questions from governed sales tables and draft a recommendation. — is testing Multi-agent Genie Space tool routing and governed answers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Embed test chunks with relevant facts at the end and confirm they remain retrievable. This action primarily relates to Embedding context window and chunk token distribution: The embedding encoder must accept the actual tokenized chunk length; silently truncating chunks can drop relevant suffix facts. However, the described case — A customer success supervisor agent must answer revenue questions from governed sales tables and draft a recommendation. — is testing Multi-agent Genie Space tool routing and governed answers. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect token volumes, QPS, endpoint pricing, cache hit rate and user/workload attribution. This action primarily relates to Databricks usage and token cost controls: Reduce LLM spend through token budgets, caching, smaller appropriate models and AI Gateway quotas while preserving acceptance quality. However, the described case — An operations assistant benchmark favors a huge model on fluency, but a smaller model has higher citation correctness at lower cost. — is testing Model selection from quantitative task metrics. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect logging configuration, sampling policy, trace privacy, missing identifiers and audit destinations. This action primarily relates to Inference logging and production trace privacy: Inference logs and tracing permit diagnosis of real issues but must be sampled, retained and redacted safely. However, the described case — An operations assistant benchmark favors a huge model on fluency, but a smaller model has higher citation correctness at lower cost. — is testing Model selection from quantitative task metrics. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest diagnose choice for this specific question. Inspect gateway policies, invocation identity, consumption tables, endpoint errors and supported feature availability. This action primarily relates to AI Gateway rate limits, usage tables and inference telemetry: AI Gateway offers controlled access, supported usage tracking and rate limiting to protect shared endpoints and measure consumption. However, the described case — An operations assistant benchmark favors a huge model on fluency, but a smaller model has higher citation correctness at lower cost. — is testing Model selection from quantitative task metrics. It does not directly establish the diagnose outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest safeguard choice for this specific question. Retain a sampling and redaction policy for traces rather than logging raw secrets in production. This action primarily relates to MLflow tracing in an Agent Framework workflow: MLflow tracing records agent steps, tool calls and latency; integration must retain child spans and inputs without leaking secrets. However, the described case — A customer success supervisor agent must answer revenue questions from governed sales tables and draft a recommendation. — is testing Multi-agent Genie Space tool routing and governed answers. It does not directly establish the safeguard outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest safeguard choice for this specific question. Use task-appropriate sampling and assess diversity and usefulness rather than exact wording alone. This action primarily relates to Temperature and deterministic format tradeoffs: Decoding settings can affect variability and cost but cannot replace validation, grounding or application logic. However, the described case — A customer success supervisor agent must answer revenue questions from governed sales tables and draft a recommendation. — is testing Multi-agent Genie Space tool routing and governed answers. It does not directly establish the safeguard outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Load-test representative search traffic and benchmark index freshness versus storage/serving cost. This action primarily relates to Standard versus storage-optimized Vector Search index: Index choice trades capacity, update speed, latency and storage economics; availability and feature support may vary by cloud/region. However, the described case — A legacy SQL Classic warehouse cannot run a new `ai_query()` workload and engineers assume bad model credentials. — is testing SQL ai_query for governed batch inference. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Exercise failed tests and confirm release is blocked, then deploy and verify a passing version. This action primarily relates to CI/CD pipelines for retrieval and agent components: Production promotions should test source index, prompt, model packaging and tool behavior independently before release. However, the described case — A legacy SQL Classic warehouse cannot run a new `ai_query()` workload and engineers assume bad model credentials. — is testing SQL ai_query for governed batch inference. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.

Not the strongest verify choice for this specific question. Load the logged pyfunc model in an isolated environment and test the declared signature on sample requests. This action primarily relates to MLflow pyfunc packaging for pre- and post-processing: A custom MLflow pyfunc model wraps request parsing, retriever/model invocation and output formatting inside a reproducible predict interface. However, the described case — A legacy SQL Classic warehouse cannot run a new `ai_query()` workload and engineers assume bad model credentials. — is testing SQL ai_query for governed batch inference. It does not directly establish the verify outcome or constraint that the question requires; it may still be useful in a different stage or incident.