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Choose suitable managed APIs or low-code/no-code tools for approved prototypes, while keeping security, validation and deployment responsibilities explicit. This step is associated with Low-code and API access as development choices, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Agent Search for governed enterprise retrieval and public Google Search for externally available web facts, confirming relevant authority and availability. Plus: Inspect source provenance, whether content is internal or public, indexing and access policy, and freshness expectations. Scenario: A policy assistant needs the latest confidential employee travel reimbursement rule.

Inspect audience, tasks, customization scope, sharing policy, confidential inputs and the need for programmatic integrations. This step is associated with Gemini app and Gems personal productivity, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Agent Search for governed enterprise retrieval and public Google Search for externally available web facts, confirming relevant authority and availability. Plus: Inspect source provenance, whether content is internal or public, indexing and access policy, and freshness expectations. Scenario: A policy assistant needs the latest confidential employee travel reimbursement rule.

Summaries cite allowed sources, generated assets meet brand rules, and state-changing tasks require verified authorization. This step is associated with Summarize discover create and automate use cases, but it is not one of the two complementary requirements here. The intended response and verification are: Choose Agent Search for governed enterprise retrieval and public Google Search for externally available web facts, confirming relevant authority and availability. Plus: Inspect source provenance, whether content is internal or public, indexing and access policy, and freshness expectations. Scenario: A policy assistant needs the latest confidential employee travel reimbursement rule.

The chosen model meets modality and hosting constraints without confusing an open-weights release with a fully managed enterprise service. This result assesses Gemini multimodal versus Gemma open models, not the business outcome under test. The relevant acceptance condition is: Generated visual assets meet the creative brief without claiming the model retrieved authentic original photographs. Scenario: A marketer mistakes generated concept art for an untouched photograph of a real customer event.

The model reaches production only after traceable data and testing gates, and a deteriorating release is detected and corrected. This result assesses ML lifecycle from ingestion to management, not the business outcome under test. The relevant acceptance condition is: Generated visual assets meet the creative brief without claiming the model retrieved authentic original photographs. Scenario: A marketer mistakes generated concept art for an untouched photograph of a real customer event.

Only necessary lawful inputs reach the model, and users are not exposed through recoverable identifiers or leaked outputs. This result assesses Privacy minimization anonymization and pseudonymization, not the business outcome under test. The relevant acceptance condition is: Generated visual assets meet the creative brief without claiming the model retrieved authentic original photographs. Scenario: A marketer mistakes generated concept art for an untouched photograph of a real customer event.

A query about current policies returns the valid version or an explicit limitation when evidence is absent. This result assesses Knowledge cutoff and fresh business information, not the business outcome under test. The relevant acceptance condition is: Quality regressions trigger investigation or rollback while business and safety metrics remain measurable. Scenario: A recommendation tool's accuracy drops when product categories change during peak season.

Answers refer to the correct approved enterprise source or declare insufficient evidence while enforcing permissions. This result assesses First-party enterprise grounding and source access, not the business outcome under test. The relevant acceptance condition is: Quality regressions trigger investigation or rollback while business and safety metrics remain measurable. Scenario: A recommendation tool's accuracy drops when product categories change during peak season.

The selected infrastructure supports the required AI service with observed capacity and cost without assuming a TPU is an application. This result assesses AI-optimized infrastructure accelerators and capacity, not the business outcome under test. The relevant acceptance condition is: Quality regressions trigger investigation or rollback while business and safety metrics remain measurable. Scenario: A recommendation tool's accuracy drops when product categories change during peak season.

The model meets agreed fairness and error thresholds across supported groups and escalates poorly represented cases. This result assesses Bias fairness and edge case evaluation, not the business outcome under test. The relevant acceptance condition is: The model produces the specified output on varied evaluation inputs without merely copying demonstration examples. Scenario: A claims processor misclassifies rare document layouts until varied labeled examples are added to the prompt.

Each stage passes checked results to the next and failures are surfaced without corrupting later outputs. This result assesses Prompt chaining and staged transformations, not the business outcome under test. The relevant acceptance condition is: The model produces the specified output on varied evaluation inputs without merely copying demonstration examples. Scenario: A claims processor misclassifies rare document layouts until varied labeled examples are added to the prompt.

Only necessary lawful inputs reach the model, and users are not exposed through recoverable identifiers or leaked outputs. This result assesses Privacy minimization anonymization and pseudonymization, not the business outcome under test. The relevant acceptance condition is: The model produces the specified output on varied evaluation inputs without merely copying demonstration examples. Scenario: A claims processor misclassifies rare document layouts until varied labeled examples are added to the prompt.

Use Security Command Center and suitable operational monitoring to surface cloud security posture and findings, then assign remediation and verify fixes. This response is aimed at Security Command Center and workload monitoring, not the controlling requirement here. Security tools aid detection and prioritization; deploying a dashboard does not automatically harden an exposed AI workload. The targeted response is: Use Agent Assist when the goal is real-time suggestions, relevant knowledge and summaries to support a human agent, not to replace the agent's authorization. Scenario: A business manager assumes Agent Assist should complete every high-value refund without an operator decision.

Choose Speech-to-Text, Text-to-Speech, Translation and related APIs for specialized audio and language operations, validating quality and customer context. This response is aimed at Speech translation and language tool services, not the controlling requirement here. Specialized AI APIs address distinct input and output tasks; an unverified transcript can corrupt later generated summaries. The targeted response is: Use Agent Assist when the goal is real-time suggestions, relevant knowledge and summaries to support a human agent, not to replace the agent's authorization. Scenario: A business manager assumes Agent Assist should complete every high-value refund without an operator decision.

Choose Agent Search for governed enterprise retrieval and public Google Search for externally available web facts, confirming relevant authority and availability. This response is aimed at Agent Search versus public Google Search, not the controlling requirement here. Public web results cannot prove internal enterprise policy; private search indexes must honor access control and freshness. The targeted response is: Use Agent Assist when the goal is real-time suggestions, relevant knowledge and summaries to support a human agent, not to replace the agent's authorization. Scenario: A business manager assumes Agent Assist should complete every high-value refund without an operator decision.

The model produces the specified output on varied evaluation inputs without merely copying demonstration examples. This result assesses Zero shot one shot and few shot prompting, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: A team proposes model fine-tuning every day to keep a fast-changing inventory list current.

An answer based on current public material includes suitable source evidence without presenting it as private company policy. This result assesses Grounding with Google Search for public current facts, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: A team proposes model fine-tuning every day to keep a fast-changing inventory list current.

The system preserves correct table relationships and document evidence with traceable origin and consistent permissions. This result assesses Structured and unstructured data distinctions, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: A team proposes model fine-tuning every day to keep a fast-changing inventory list current.

Choose grounding/RAG for changing source facts, consider fine-tuning for repeatable learned behavior with suitable examples and keep human review for high-impact uncertain results. This step is associated with Fine-tuning versus retrieval and human review, but it is not one of the two complementary requirements here. The intended response and verification are: Use Grounding with Google Search for relevant current publicly accessible information, distinct from private enterprise retrieval. Plus: Inspect whether the question requires public updates, citation freshness, source authority and outbound search policy. Scenario: A market analyst asks for a recently announced publicly listed product feature.

Only necessary lawful inputs reach the model, and users are not exposed through recoverable identifiers or leaked outputs. This step is associated with Privacy minimization anonymization and pseudonymization, but it is not one of the two complementary requirements here. The intended response and verification are: Use Grounding with Google Search for relevant current publicly accessible information, distinct from private enterprise retrieval. Plus: Inspect whether the question requires public updates, citation freshness, source authority and outbound search policy. Scenario: A market analyst asks for a recently announced publicly listed product feature.

Inspect prompt task statement, example diversity, expected response format, edge cases and token cost. This step is associated with Zero shot one shot and few shot prompting, but it is not one of the two complementary requirements here. The intended response and verification are: Use Grounding with Google Search for relevant current publicly accessible information, distinct from private enterprise retrieval. Plus: Inspect whether the question requires public updates, citation freshness, source authority and outbound search policy. Scenario: A market analyst asks for a recently announced publicly listed product feature.

Only necessary lawful inputs reach the model, and users are not exposed through recoverable identifiers or leaked outputs. This result assesses Privacy minimization anonymization and pseudonymization, not the business outcome under test. The relevant acceptance condition is: Authorized users and services reach only the minimum required resources, and cross-team access attempts fail predictably. Scenario: A customer analytics bot tries to query another business unit's restricted BigQuery dataset.

The selected studio supports the intended prototype and has a documented route to governed production operations. This result assesses Agent Studio versus Google AI Studio environment selection, not the business outcome under test. The relevant acceptance condition is: Authorized users and services reach only the minimum required resources, and cross-team access attempts fail predictably. Scenario: A customer analytics bot tries to query another business unit's restricted BigQuery dataset.

The selected pilot solves a documented problem and outperforms an appropriate non-AI baseline on agreed measures. This result assesses Business use case selection and measurable need, not the business outcome under test. The relevant acceptance condition is: Authorized users and services reach only the minimum required resources, and cross-team access attempts fail predictably. Scenario: A customer analytics bot tries to query another business unit's restricted BigQuery dataset.

Use Grounding with Google Search for relevant current publicly accessible information, distinct from private enterprise retrieval. This step is associated with Grounding with Google Search for public current facts, but it is not one of the two complementary requirements here. The intended response and verification are: Use ReAct-style alternating decision, tool action and observation for tasks that require external information, while bounding tool use and validating final results. Plus: Inspect necessary tools, observed tool outputs, action authorization, stopping limits and evidence-grounded final responses. Scenario: A team uses a free-form chain of reasoning to claim a reservation exists despite a failed booking tool.

Inspect internal source owner, document revision, access grants, retrieval match and citation coverage. This step is associated with First-party enterprise grounding and source access, but it is not one of the two complementary requirements here. The intended response and verification are: Use ReAct-style alternating decision, tool action and observation for tasks that require external information, while bounding tool use and validating final results. Plus: Inspect necessary tools, observed tool outputs, action authorization, stopping limits and evidence-grounded final responses. Scenario: A team uses a free-form chain of reasoning to claim a reservation exists despite a failed booking tool.

The selected creation path delivers the required functionality and an auditable production boundary without unnecessary bespoke infrastructure. This step is associated with Low-code and API access as development choices, but it is not one of the two complementary requirements here. The intended response and verification are: Use ReAct-style alternating decision, tool action and observation for tasks that require external information, while bounding tool use and validating final results. Plus: Inspect necessary tools, observed tool outputs, action authorization, stopping limits and evidence-grounded final responses. Scenario: A team uses a free-form chain of reasoning to claim a reservation exists despite a failed booking tool.

The model meets agreed fairness and error thresholds across supported groups and escalates poorly represented cases. This step is associated with Bias fairness and edge case evaluation, but it is not one of the two complementary requirements here. The intended response and verification are: Improve instructions, examples and context through prompt engineering first; consider parameter-efficient tuning only when the model and business case justify trained adaptations. Plus: Compare quality under revised prompt examples, data needs, tuning support, costs, rollback and evaluation outcomes. Scenario: A recurring enterprise task still fails despite tested prompting, and a sponsor requests a justified customization assessment.

Compare target users, available modalities, licensing, data policy, throughput budget and verified trial results. This step is associated with Foundation model portfolio choice by business case, but it is not one of the two complementary requirements here. The intended response and verification are: Improve instructions, examples and context through prompt engineering first; consider parameter-efficient tuning only when the model and business case justify trained adaptations. Plus: Compare quality under revised prompt examples, data needs, tuning support, costs, rollback and evaluation outcomes. Scenario: A recurring enterprise task still fails despite tested prompting, and a sponsor requests a justified customization assessment.

Select a generative model when the business requires newly composed text or other content; use classification or regression when the outcome is an existing label or number. This step is associated with Distinguish generative AI from classification AI, but it is not one of the two complementary requirements here. The intended response and verification are: Improve instructions, examples and context through prompt engineering first; consider parameter-efficient tuning only when the model and business case justify trained adaptations. Plus: Compare quality under revised prompt examples, data needs, tuning support, costs, rollback and evaluation outcomes. Scenario: A recurring enterprise task still fails despite tested prompting, and a sponsor requests a justified customization assessment.

An answer based on current public material includes suitable source evidence without presenting it as private company policy. This result assesses Grounding with Google Search for public current facts, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: An automated loan decision process needs a human approval checkpoint for unusual high-impact cases.

The solution creates useful draft content only where permitted, while fixed-category decisions remain measurable and testable. This result assesses Distinguish generative AI from classification AI, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: An automated loan decision process needs a human approval checkpoint for unusual high-impact cases.

The agent calls only permitted tools, incorporates returned evidence and stops safely when a dependency fails. This result assesses ReAct planning with tool observation, not the business outcome under test. The relevant acceptance condition is: The improvement method addresses the actual deficiency without relying on model training to update a frequently changing price list. Scenario: An automated loan decision process needs a human approval checkpoint for unusual high-impact cases.