Databricks Certified Generative AI Engineer Associate Bundle

Not the strongest diagnose choice for this specific question. Inspect prompt names, version history, aliases, tests and active referenced URI. This action primarily relates to MLflow Prompt Registry version aliases and promotion: Prompt Registry tracks immutable prompt versions with mutable aliases for staging and production, enabling change control and rollback. However, the described case — A new model signature expects messages array but the existing app sends only a prompt string. — 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 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 new model signature expects messages array but the existing app sends only a prompt string. — 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 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 — A new model signature expects messages array but the existing app sends only a prompt string. — 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. Deploy synchronized index definitions and validate source freshness during production cutover. 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 policy chatbot creates vectors but never passes retrieved passages to the LLM, producing unsupported recommendations. — 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. Run the supported index update/sync process and verify the indexed source identifiers reflect current Delta records. This action primarily relates to Databricks Vector Search index from governed Delta data: A vector index serves embeddings of chunked records maintained in a Unity Catalog governed Delta source table. However, the described case — A policy chatbot creates vectors but never passes retrieved passages to the LLM, producing unsupported recommendations. — 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. Benchmark a supported index type and provision for the expected vector count, latency and update pattern. 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 policy chatbot creates vectors but never passes retrieved passages to the LLM, producing unsupported recommendations. — 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. Split by task-relevant sections and filter top passages before prompt assembly. This action primarily relates to Chunk quality evaluation and boundary splitting: Retrieval failure can arise from overly small chunks that omit needed context or overly large chunks that dilute relevance. However, the described case — A LangChain workflow sends a dictionary of question and context to a prompt that expects only input, raising a key error. — is testing LangChain runnable composition and invocation. 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. Validate account and order IDs, enforce identity scope, and retrieve the matching order status before generation. This action primarily relates to Query-field driven prompt and retrieval filters: User intent and structured fields such as account_id should drive authorized SQL lookups or retrieval filters, not be ignored in free text. However, the described case — A LangChain workflow sends a dictionary of question and context to a prompt that expects only input, raising a key error. — is testing LangChain runnable composition and invocation. 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. Revise few-shot examples for format compliance and rerun previously passing examples. This action primarily relates to Prompt changes evaluated against baseline: A prompt improvement should be measured against a fixed baseline set, not accepted because a few outputs sound better. However, the described case — A LangChain workflow sends a dictionary of question and context to a prompt that expects only input, raising a key error. — is testing LangChain runnable composition and invocation. 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 user conversation state, current intent, entity resolution and selected tool trace. 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 search result includes fake 'system message' text requesting the assistant reveal private SQL results. — is testing Prompt-injection defenses for retrieved context. 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 adversarial prompt examples, tool permissions and whether refusals are enforced outside the model. This action primarily relates to Unsafe model output and jailbreak response diagnosis: User-provided instructions may attempt to bypass agent policies or cause data leakage; classification and tool boundaries need separate tests. However, the described case — A search result includes fake 'system message' text requesting the assistant reveal private SQL results. — is testing Prompt-injection defenses for retrieved context. 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 whether the task requires real external state, permitted action or just text transformation. 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 search result includes fake 'system message' text requesting the assistant reveal private SQL results. — is testing Prompt-injection defenses for retrieved context. 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. Promote a tracked model version or alias while preserving the previous model version for rollback. 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 new Vector Search index is created in staging but production points at its previous source table version. — is testing CI/CD pipelines for retrieval and agent components. 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 new Vector Search index is created in staging but production points at its previous source table version. — is testing CI/CD pipelines for retrieval and agent components. 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. Log the app with dependency requirements, retrieval configuration, model signature and example requests. 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 new Vector Search index is created in staging but production points at its previous source table version. — is testing CI/CD pipelines for retrieval and agent components. 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 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 — An order-support assistant knows return policy but cannot answer delivery ETA because shipment status is absent. — is testing Authoritative source coverage for factual answers. 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 MIME types, text-layer availability, encoding and parsing failures per source. This action primarily relates to Format-specific document extraction and OCR needs: Extract text with a parser suited to source format; image-only scans require OCR rather than HTML or plain-text parsing. However, the described case — An order-support assistant knows return policy but cannot answer delivery ETA because shipment status is absent. — is testing Authoritative source coverage for factual answers. 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 lexical token overlap, dense distance, metadata predicates and index access control. This action primarily relates to Hybrid sparse and dense retrieval with filters: Hybrid search combines lexical exact-match and semantic similarity; metadata filtering narrows authorized relevant content. However, the described case — An order-support assistant knows return policy but cannot answer delivery ETA because shipment status is absent. — is testing Authoritative source coverage for factual answers. 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. Treat absence of relevant retrieved evidence as an abstention condition, not a reason to invent missing facts. 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 — An HR help desk says it wants 'AI answers' without specifying which employee documents users may see. — is testing Translate use-case requirements into pipeline inputs and outputs. 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. Keep instructions and examples versioned and test new patterns without allowing retrieved data to override policy. 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 — An HR help desk says it wants 'AI answers' without specifying which employee documents users may see. — is testing Translate use-case requirements into pipeline inputs and outputs. 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 use a document-answering assistant as an autonomous transaction engine without explicit tools and approvals. This action primarily relates to Agent Bricks Knowledge Assistant suitability: A governed knowledge assistant grounds conversational responses in curated documents and access-controlled retrieval. However, the described case — An HR help desk says it wants 'AI answers' without specifying which employee documents users may see. — is testing Translate use-case requirements into pipeline inputs and outputs. 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. Compare fixed evaluation queries on baseline and candidate retrieval with confidence in differences. 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 — PDF extraction repeats a footer classification notice inside every employee policy chunk. — is testing Boilerplate and navigation filtering in source corpora. 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 representative queries needing missing records and confirm either accurate retrieval or abstention. This action primarily relates to Authoritative source coverage for factual answers: A RAG model cannot answer accurately if the required authoritative record is missing or stale in its index. However, the described case — PDF extraction repeats a footer classification notice inside every employee policy chunk. — is testing Boilerplate and navigation filtering in source corpora. 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 ablation tests with and without reranking using the same labeled query set and latency budget. 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 — PDF extraction repeats a footer classification notice inside every employee policy chunk. — is testing Boilerplate and navigation filtering in source corpora. 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 — An engineer changes the source chunking but keeps old stored embeddings and assumes retrieval reflects the new boundaries. — is testing Embedding model swap and reindex 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. Use the same quality rubrics for pre-release gates and production monitoring with separate datasets. 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 — An engineer changes the source chunking but keeps old stored embeddings and assumes retrieval reflects the new boundaries. — is testing Embedding model swap and reindex 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. 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 — An engineer changes the source chunking but keeps old stored embeddings and assumes retrieval reflects the new boundaries. — is testing Embedding model swap and reindex 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 tool-call traces, retrieved source text and permission decisions for instruction-origin confusion. 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 asks about 'the previous invoice' but the agent does not carry the prior invoice ID to the next turn. — is testing Conversation context and intent routing. 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 nested traces, span order, tool duration and captured payload security. 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 asks about 'the previous invoice' but the agent does not carry the prior invoice ID to the next turn. — is testing Conversation context and intent routing. 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 customer asks about 'the previous invoice' but the agent does not carry the prior invoice ID to the next turn. — is testing Conversation context and intent routing. 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. Check embedding dimensions or extracted-field validation against task-specific acceptance tests. This action primarily relates to Specialized model versus general LLM: Embedding, reranking, extraction and generative models solve different stages and should be evaluated for their actual role. However, the described case — A large catalog's vector storage bill rises after selecting a high-dimensional encoder with no measured gain. — is testing Embedding dimensions latency and index storage. 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. Verify matched query and document vectors use the approved version and retrieval improves on held-out examples. 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 large catalog's vector storage bill rises after selecting a high-dimensional encoder with no measured gain. — is testing Embedding dimensions latency and index storage. 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 large catalog's vector storage bill rises after selecting a high-dimensional encoder with no measured gain. — is testing Embedding dimensions latency and index storage. 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.