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 — A production team passes around unversioned notebook checkpoints and cannot reproduce the active agent package. — 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 — A production team passes around unversioned notebook checkpoints and cannot reproduce the active agent package. — 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 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 production team passes around unversioned notebook checkpoints and cannot reproduce the active agent package. — 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 safeguard choice for this specific question. Pin dependency and prompt schemas across releases so library updates do not silently break chain invocation. 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 long regulation section exceeds an encoder's limit and the final exception is never embedded. — is testing Embedding context window and chunk token distribution. 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. Isolate instructions from source text and reject cross-trust-boundary requests through policy/tool checks. 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 long regulation section exceeds an encoder's limit and the final exception is never embedded. — is testing Embedding context window and chunk token distribution. 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 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 energy tariff PDF is chunked by arbitrary text lines and rate values lose the currency and zone column. — is testing Table-aware chunking and metadata-rich retrieval. 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 energy tariff PDF is chunked by arbitrary text lines and rate values lose the currency and zone column. — is testing Table-aware chunking and metadata-rich retrieval. 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 — An energy tariff PDF is chunked by arbitrary text lines and rate values lose the currency and zone column. — is testing Table-aware chunking and metadata-rich retrieval. 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 card numbers and nonessential identity fields before ingestion and keep approved case references separately. 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 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 server-side ownership checks and transaction approval on the refund tool in addition to input screening. 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 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. Differentiate direct identifiers from permissible temporal fields and apply purpose-specific protection. This action primarily relates to Contextual masking versus destructive data removal: Masking must preserve information needed for the task, such as non-sensitive dates or amounts, and cannot replace access control. 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 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. Sample indexed chunks against a rights manifest and confirm prohibited sources are excluded. This action primarily relates to Training and retrieval corpus license rights: Public accessibility does not automatically authorize copying, embedding or using content in commercial RAG services. However, the described case — A financial invoice extractor masks every number, including legitimate tax amounts and invoice totals. — is testing Contextual masking versus destructive data removal. 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 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 financial invoice extractor masks every number, including legitimate tax amounts and invoice totals. — is testing Contextual masking versus destructive data removal. 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 financial invoice extractor masks every number, including legitimate tax amounts and invoice totals. — is testing Contextual masking versus destructive data removal. 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. Log model/prompt/data identifiers and metrics to a run with reproducible evaluation artifacts. 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 support assistant retrieves a full 60-page manual and overwhelms the model prompt with unrelated sections. — 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 support assistant retrieves a full 60-page manual and overwhelms the model prompt with unrelated sections. — 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 support assistant retrieves a full 60-page manual and overwhelms the model prompt with unrelated sections. — 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. 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 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 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. Increase coherent chunk context or parent expansion, then compare answer coverage to the earlier chunking strategy. 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 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 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. Extract model/country fields and apply them as verified metadata filters to approved retrieval. 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 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 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. Do not call a one-time benchmark a complete monitoring program for a deployed agent. 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 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. 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 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 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. Retest prompt injection protections whenever new retrieval connectors or tools are introduced. 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 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 — A research bot reports outdated product prices because the indexed catalog is a quarterly snapshot. — is testing Authoritative source coverage for factual 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. Check that an exception and its definition are available to the generator after retrieval. 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 research bot reports outdated product prices because the indexed catalog is a quarterly snapshot. — is testing Authoritative source coverage for factual 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. Compare hit recall and answer grounding for sample chunk sizes with a fixed query set. This action primarily relates to Chunk size and overlap tradeoffs: Chunk size determines the number of vectors and coherence; overlap reduces boundary loss but increases index size and cost. However, the described case — A research bot reports outdated product prices because the indexed catalog is a quarterly snapshot. — is testing Authoritative source coverage for factual 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. Sample pages/images against extracted text and check structured fields and layout fidelity. 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 — 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 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. Compare hit recall and answer grounding for sample chunk sizes with a fixed query set. This action primarily relates to Chunk size and overlap tradeoffs: Chunk size determines the number of vectors and coherence; overlap reduces boundary loss but increases index size and cost. 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 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 — 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 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 an available Foundation Model API endpoint with compatible authentication, task API and quotas. 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 LangChain pipeline returns an SDK message object while its API gateway expects a plain answer string. — is testing Simple prompt-retrieval-generation chain assembly. 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. Treat prompt alias updates as change-controlled deployments with regression gates and auditing. 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 LangChain pipeline returns an SDK message object while its API gateway expects a plain answer string. — is testing Simple prompt-retrieval-generation chain assembly. 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.