Databricks Certified Generative AI Engineer Associate Bundle

Not the strongest diagnose choice for this specific question. Inspect required model type, deployment control, supported region, endpoint quotas and operational burden. This action primarily relates to Foundation Model API versus custom serving choices: Databricks Foundation Model APIs offer managed access to supported models; custom serving is suitable for specialized models or control needs. 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 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 logged model artifacts, environment requirements, signature and source index identifiers. This action primarily relates to RAG production bundle metadata and dependencies: A deployable RAG application needs tracked model code, retriever config, embedding model, dependency versions, signature and example input. 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 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 UC model names, signatures, version metadata, lineage and relevant ACLs. 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 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 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. Compare compact versus larger embeddings on labeled retrieval quality and total vector-index footprint. This action primarily relates to Embedding dimensions latency and index storage: Embedding width affects index storage and computation, and larger dimensions do not automatically improve task relevance. However, the described case — A troubleshooting assistant retrieves a part number but omits the note that the part is discontinued. — is testing Chunk quality evaluation and boundary splitting. 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. Choose a compatible context-length model or rechunk with measured token counts before embedding. 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 troubleshooting assistant retrieves a part number but omits the note that the part is discontinued. — is testing Chunk quality evaluation and boundary splitting. 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. Build a compatible new index and re-embed the corpus, then test cutover against representative queries. This action primarily relates to Embedding model swap and reindex compatibility: Changed embedding dimensions or model semantics typically require re-embedding and index consistency before comparing search quality. However, the described case — A troubleshooting assistant retrieves a part number but omits the note that the part is discontinued. — is testing Chunk quality evaluation and boundary splitting. 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. Test data questions for correct Genie routing, SQL-backed values and denial of unauthorized tables. This action primarily relates to Multi-agent Genie Space tool routing and governed answers: Genie Spaces can provide governed natural-language access to structured business metrics; an agent should choose authorized Genie tools over guessing SQL facts. However, the described case — A benefits RAG assistant cites a real policy page but its claimed deadline does not appear in the cited passage. — is testing Hallucinated facts and unsupported citations. 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 representative task evaluation in the intended language and document limitations. 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 benefits RAG assistant cites a real policy page but its claimed deadline does not appear in the cited passage. — is testing Hallucinated facts and unsupported citations. 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 a topic switch and anaphoric reference to ensure correct routing and no cross-session state bleed. This action primarily relates to Conversation context and intent routing: Multi-turn assistants need explicit conversational state while routing each new request to the correct tool or topic. However, the described case — A benefits RAG assistant cites a real policy page but its claimed deadline does not appear in the cited passage. — is testing Hallucinated facts and unsupported citations. 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 implement choice for this specific question. Configure approved inference tables and trace metadata to correlate outputs with deployed model/index versions. This action primarily relates to Inference tables and Agent Monitoring feedback loop: Inference tables and agent monitoring capture production request/response signals and traces for quality investigations. However, the described case — One department floods a shared foundation-model endpoint and starves other internal agents. — is testing AI Gateway rate limits, usage tables and inference telemetry. 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. Use a fit-for-task model, batching and usage thresholds with per-request cost monitoring. 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 — One department floods a shared foundation-model endpoint and starves other internal agents. — is testing AI Gateway rate limits, usage tables and inference telemetry. 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. Add groundedness and policy-error monitoring with escalation thresholds alongside latency. 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 — One department floods a shared foundation-model endpoint and starves other internal agents. — is testing AI Gateway rate limits, usage tables and inference telemetry. 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 paired representative requests and record quality versus p95 latency and cost tradeoffs. This action primarily relates to Model selection by quality latency and budget: Select the smallest suitable model that meets a task's accuracy and latency constraints under realistic workload, not marketing rank. However, the described case — A brainstorming app wants creative draft ideas but is incorrectly evaluated on exact-string matching to one reference. — is testing Temperature and deterministic format tradeoffs. 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. 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 brainstorming app wants creative draft ideas but is incorrectly evaluated on exact-string matching to one reference. — is testing Temperature and deterministic format tradeoffs. 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. Rerun a controlled experiment and confirm metrics match within expected stochastic tolerance. This action primarily relates to Experiment tracking and version reproducibility: MLflow experiments should retain prompt, model, dataset, parameters and evaluation outputs to support a defensible comparison. However, the described case — A brainstorming app wants creative draft ideas but is incorrectly evaluated on exact-string matching to one reference. — is testing Temperature and deterministic format tradeoffs. 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 capacity caps, sync behavior, qps, p95 latency and expected update 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 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 predict entrypoint, model artifacts, dependency environment, inputs and output serialization. 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 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 auth model, frontend token handling, backend endpoint calls and user-level data controls. This action primarily relates to Databricks Apps user-facing agent interface and OAuth identities: Databricks Apps supports authenticated interactive frontends; app credentials and user credentials have different scopes. 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 diagnose 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 harmful queries and verify contaminated passages are blocked without discarding legitimate safety instructions. This action primarily relates to Toxic, inaccurate and adversarial corpus cleaning: Unreliable source text can teach unsafe behavior through retrieval; quarantine and source review matter before embeddings. However, the described case — A partner PDF includes text telling a RAG assistant to reveal its secret connection string in the answer. — is testing Input guardrails against prompt-injected source documents. 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. Query for known synthetic sensitive strings and confirm no original identifiers appear in chunks or answers. This action primarily relates to Sensitive-field masking before embedding: Masking or tokenization can remove unnecessary PII from chunks before embedding and external model calls while retaining useful meaning. However, the described case — A partner PDF includes text telling a RAG assistant to reveal its secret connection string in the answer. — is testing Input guardrails against prompt-injected source documents. 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 malicious requests with valid JSON tool calls and confirm the tool rejects unauthorized actions. This action primarily relates to Prompt safety filtering and tool allowlists: Content filters and action allowlists are complementary; a moderation score is not a substitute for authorization of external effects. However, the described case — A partner PDF includes text telling a RAG assistant to reveal its secret connection string in the answer. — is testing Input guardrails against prompt-injected source documents. 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 implement choice for this specific question. Evaluate a summarization model against faithful concise summaries with source references. This action primarily relates to Task choice: classification versus generative summarization: Classification predicts a category whereas summarization compresses meaning; the desired output determines the appropriate task. However, the described case — A monthly reporting assistant processes thousands of independent documents overnight with low urgency. — is testing Latency, cost and retrieval targets as design requirements. 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. Specify a stable array-of-objects schema, require only the specified keys and reject malformed results before rendering. This action primarily relates to Structured JSON schema prompt and format compliance: A model can produce plausible prose rather than machine-readable JSON unless the output contract and validation are explicit. However, the described case — A monthly reporting assistant processes thousands of independent documents overnight with low urgency. — is testing Latency, cost and retrieval targets as design requirements. 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. Split read-only diagnostic tools from authorized destructive operations with separate permissions. This action primarily relates to Agent tool schemas, least privilege and retries: Well-described tool schemas constrain parameters, and idempotent retries prevent duplicate side effects. However, the described case — A monthly reporting assistant processes thousands of independent documents overnight with low urgency. — is testing Latency, cost and retrieval targets as design requirements. 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 diagnose choice for this specific question. Inspect required model type, deployment control, supported region, endpoint quotas and operational burden. This action primarily relates to Foundation Model API versus custom serving choices: Databricks Foundation Model APIs offer managed access to supported models; custom serving is suitable for specialized models or control needs. However, the described case — A support agent times out at peak demand because requests exceed the configured endpoint throughput. — is testing Endpoint scale concurrency and operational rollout. 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 warehouse type, runtime compatibility, endpoint status, SQL arguments and batch size. This action primarily relates to SQL ai_query for governed batch inference: `ai_query()` invokes an available serving endpoint from SQL; suitability depends on supported runtimes, endpoint and workload. However, the described case — A support agent times out at peak demand because requests exceed the configured endpoint throughput. — is testing Endpoint scale concurrency and operational rollout. 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 capacity caps, sync behavior, qps, p95 latency and expected update 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 support agent times out at peak demand because requests exceed the configured endpoint throughput. — is testing Endpoint scale concurrency and operational rollout. 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. 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 maintenance assistant queries a vector index by exact serial number without preserving metadata filters. — is testing Vector Search retrieval workflow and query integration. 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. Pin compatible dependencies and rebuild/evaluate the serving artifact before promoting a new environment. This action primarily relates to Deployed model dependency drift and environment compatibility: Models logged in experiments can fail in serving if Python dependencies or inference signatures differ between build and target. However, the described case — A maintenance assistant queries a vector index by exact serial number without preserving metadata filters. — is testing Vector Search retrieval workflow and query integration. 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. Use `ai_query()` against a supported endpoint in a controlled batch-inference query with result checks. This action primarily relates to SQL ai_query for governed batch inference: `ai_query()` invokes an available serving endpoint from SQL; suitability depends on supported runtimes, endpoint and workload. However, the described case — A maintenance assistant queries a vector index by exact serial number without preserving metadata filters. — is testing Vector Search retrieval workflow and query integration. 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. Test a missing-ground-truth record to verify the judge abstains or uses the appropriate alternate scorer. This action primarily relates to Ground-truth requirement for correctness versus reference-free judges: A correctness comparison often needs an expected answer; other judges such as format or safety can operate without a reference. However, the described case — A medical expert flags unsafe triage advice, but engineering discards the feedback because an LLM judge rates it highly. — is testing Human SME feedback rubric and reviewer calibration. 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. 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 medical expert flags unsafe triage advice, but engineering discards the feedback because an LLM judge rates it highly. — is testing Human SME feedback rubric and reviewer calibration. 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. Query sampled live traces and demonstrate a complaint can be reproduced and scored on an offline test. This action primarily relates to Inference tables and Agent Monitoring feedback loop: Inference tables and agent monitoring capture production request/response signals and traces for quality investigations. However, the described case — A medical expert flags unsafe triage advice, but engineering discards the feedback because an LLM judge rates it highly. — is testing Human SME feedback rubric and reviewer calibration. 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.