Databricks Certified Generative AI Engineer Associate Bundle

Not the strongest diagnose choice for this specific question. Inspect deployment manifests, tests, aliases, index versions and rollback dependency matrix. 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 — An agent promotion script overwrites the only model file without retaining the prior validated artifact. — is testing Unity Catalog registered model version and privileges. 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 query embedding provenance, index schema, similarity results and selected context window. 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 — An agent promotion script overwrites the only model file without retaining the prior validated artifact. — is testing Unity Catalog registered model version and privileges. 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 serving endpoint permissions, token path, user/app identity and downstream Unity Catalog access. This action primarily relates to Model Serving endpoint permissions and caller identity: Access to a serving endpoint is governed by workspace rights and identity; caller authorization is distinct from data-source permissions. However, the described case — An agent promotion script overwrites the only model file without retaining the prior validated artifact. — is testing Unity Catalog registered model version and privileges. 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. Add concise role instructions and representative input/output examples while keeping untrusted claim text in a delimited data section. This action primarily relates to Instruction hierarchy and bounded prompt examples: Few-shot examples and explicit boundaries can clarify expected outputs, but embedded source documents should not become new system instructions. However, the described case — A refund agent issues credits before verifying invoice status or refund eligibility. — is testing Tool ordering and error isolation in multi-stage reasoning. 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 refund agent issues credits before verifying invoice status or refund eligibility. — is testing Tool ordering and error isolation in multi-stage reasoning. 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. 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 refund agent issues credits before verifying invoice status or refund eligibility. — is testing Tool ordering and error isolation in multi-stage reasoning. 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 section relationships, chunk hierarchy and whether cross-references survive extraction. This action primarily relates to Parent-child retrieval and hierarchical structure: Parent-child retrieval indexes targeted sections but expands to their broader parent context for generation. However, the described case — A manuals corpus produces 2.5 million vectors and exceeds the planned index capacity after 120-token chunks. — is testing Chunk size and overlap tradeoffs. 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 table extraction, repeated column headings, units, row identity and exact filter fields. This action primarily relates to Table-aware chunking and metadata-rich retrieval: Tabular rows need headers, units and column semantics preserved when transformed into retrieval documents. However, the described case — A manuals corpus produces 2.5 million vectors and exceeds the planned index capacity after 120-token chunks. — is testing Chunk size and overlap tradeoffs. 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 candidate-set recall, relevance scores, top-k cutoffs and added ranking latency. This action primarily relates to Cross-encoder reranking after candidate retrieval: A reranker scores query-document relevance over a candidate set; it improves ordering but does not add missing documents. However, the described case — A manuals corpus produces 2.5 million vectors and exceeds the planned index capacity after 120-token chunks. — is testing Chunk size and overlap tradeoffs. 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 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 — 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 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 paired experiments with consistent evaluation data and evaluate cost-quality Pareto tradeoffs. This action primarily relates to Model selection from quantitative task metrics: Select candidate LLMs by measured task success, latency, safety and token cost, not parameter count or leaderboard rank. 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 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 `mlflow.genai.evaluate()` on a fixed set with documented built-in and custom scorer outputs. This action primarily relates to MLflow GenAI tracing, built-in and custom scorers: MLflow GenAI traces capture components, and scorers/judges can evaluate a full answer or individual retrieval and tool spans. 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 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. Use the managed Genie connector alongside a narrowly scoped custom MCP server for ticket actions. 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 research archive contains hundreds of millions of mostly static embeddings and storage cost is the main constraint. — is testing Standard versus storage-optimized Vector Search index. 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. Restrict endpoint CAN QUERY access to approved principals and test permission boundaries. This action primarily relates to Model Serving endpoint permissions and caller identity: Access to a serving endpoint is governed by workspace rights and identity; caller authorization is distinct from data-source permissions. However, the described case — A research archive contains hundreds of millions of mostly static embeddings and storage cost is the main constraint. — is testing Standard versus storage-optimized Vector Search index. 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. Evaluate a custom serving endpoint with model packaging and scaling suitable to the checkpoint. 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 research archive contains hundreds of millions of mostly static embeddings and storage cost is the main constraint. — is testing Standard versus storage-optimized Vector Search index. 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 safeguard 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 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 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 assume a managed Foundation Model endpoint grants unrestricted training rights or satisfies data residency automatically. 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 safeguard 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 each source connector's contract, join keys, runtime error handling and the final prompt inputs. This action primarily relates to Parallel retrieval and deterministic chain postprocessing: Combining structured lookup and semantic retrieval requires explicit routing, result joining, and downstream schema checks. However, the described case — A shipping chatbot returns a list with inconsistent property names, breaking the downstream parcel status renderer. — is testing Structured JSON schema prompt and format compliance. 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. Trace which chain stage receives the question, retriever matches, prompt context and generated answer. This action primarily relates to Retriever-generator chain component boundaries: A basic RAG chain separates user input, query transformation, retrieval, prompt assembly, model generation and output validation. However, the described case — A shipping chatbot returns a list with inconsistent property names, breaking the downstream parcel status renderer. — is testing Structured JSON schema prompt and format compliance. 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 the business decision, data owners, actor permissions, known inputs and success criteria. This action primarily relates to Translate use-case requirements into pipeline inputs and outputs: Business acceptance criteria must map to concrete source inputs, generated fields, correctness measures and permission constraints. However, the described case — A shipping chatbot returns a list with inconsistent property names, breaking the downstream parcel status renderer. — is testing Structured JSON schema prompt and format compliance. 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. Score retrieved documents separately from generation using graded relevance and ranked-hit metrics. This action primarily relates to Retrieval recall precision and ranking diagnostics: Measure hit recall, precision, MRR or nDCG on labeled query-to-relevant-chunk pairs before tuning retrieval. However, the described case — A legal RAG service finds semantically similar contracts from the wrong customer workspace. — is testing Hybrid sparse and dense retrieval with filters. 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. Include part number and model metadata in each searchable row and filter by approved product line. This action primarily relates to Table-aware chunking and metadata-rich retrieval: Tabular rows need headers, units and column semantics preserved when transformed into retrieval documents. However, the described case — A legal RAG service finds semantically similar contracts from the wrong customer workspace. — is testing Hybrid sparse and dense retrieval with filters. 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. Rerank a sufficiently broad candidate pool then pass a smaller high-relevance set to the LLM. This action primarily relates to Cross-encoder reranking after candidate retrieval: A reranker scores query-document relevance over a candidate set; it improves ordering but does not add missing documents. However, the described case — A legal RAG service finds semantically similar contracts from the wrong customer workspace. — is testing Hybrid sparse and dense retrieval with filters. 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. Remove unused side-effect tools and use a plain summarization chain with bounded output. This action primarily relates to Agent tool wrappers versus direct LLM responses: A tool-using agent needs explicit callable interfaces and typed tool results, whereas plain generation requires no action execution. 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 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. Select a candidate with measured language coverage and document residual risk on target-language samples. 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 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 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. Classify current turn intent and route balances to authorized structured lookup rather than policy-only search. 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 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 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 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 deployed agent hardcodes prompt version 1, so validated updates never reach the application. — is testing MLflow Prompt Registry version aliases and promotion. 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. Start a clean container smoke test and exercise both old and new client payloads where compatibility is promised. 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 deployed agent hardcodes prompt version 1, so validated updates never reach the application. — is testing MLflow Prompt Registry version aliases and promotion. 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 registered bundle in a clean environment and validate retrieval-to-generation behavior end to end. 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 deployed agent hardcodes prompt version 1, so validated updates never reach the application. — is testing MLflow Prompt Registry version aliases and promotion. 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 data schema, primary identifiers, change detection, UC permissions and ingestion checkpoint behavior. This action primarily relates to Delta and Unity Catalog pipeline for chunk tables: A governed retrieval corpus stores chunk text, IDs, source lineage and metadata in managed Delta/Unity Catalog tables before indexing. However, the described case — A crawler fetches HTML product manuals but outputs menu text instead of article content. — is testing Format-specific document extraction and OCR needs. 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 heading structure, sentence breaks, table boundaries and parent document identifiers. This action primarily relates to Semantic boundaries versus fixed-window chunks: Headings, paragraphs and tables can require structure-aware splitting rather than arbitrary token windows. However, the described case — A crawler fetches HTML product manuals but outputs menu text instead of article content. — is testing Format-specific document extraction and OCR needs. 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 the most frequent n-grams, extracted layout artifacts and top retrieved false positives. This action primarily relates to Boilerplate and navigation filtering in source corpora: Duplicated headers, disclaimers and navigation menus contaminate embeddings and cause irrelevant hits. However, the described case — A crawler fetches HTML product manuals but outputs menu text instead of article content. — is testing Format-specific document extraction and OCR needs. 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.