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Google Generative AI Leader Practice Test 67

Question 660

The business case is: A search index ranks old duplicated material above the authoritative current page. Select TWO: the appropriate design decision and convincing validation evidence. (Select TWO.)

  1. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  2. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.
  3. A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.
  4. A parameter comparison tests repeatability, valid output length and safety outcomes for the exact selected model.
  5. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.

Explanation

Correct Answers

A:

The intervention for the stated situation A search index ranks old duplicated material above the authoritative current page is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness. It directly mitigates: Poor retrieval or missing source attribution can worsen rather than solve hallucinations.

C:

A separate outcome check for the situation A search index ranks old duplicated material above the authoritative current page is: A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim. It measures the effect of the selected intervention rather than relying on the existence of a proposal.

Incorrect Answers

B:

This choice addresses agent search prebuilt grounding versus custom rag; its main associated risk is: Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. However, the situation A search index ranks old duplicated material above the authoritative current page is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

D:

This choice addresses sampling temperature top-p and token controls; its main associated risk is: Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records. However, the situation A search index ranks old duplicated material above the authoritative current page is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

E:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A search index ranks old duplicated material above the authoritative current page is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Question 661

A business team evaluating generative AI encounters this situation: A generative answer provides a citation link that does not contain the asserted fact. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which approach is most defensible under these requirements?

  1. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  2. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.
  3. Integrate third-party content only through approved licensing, provenance and freshness controls rather than blindly trusting partner data.
  4. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Explanation

Correct Answer

A:

The decisive detail is: A generative answer provides a citation link that does not contain the asserted fact. The appropriate business or technical decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness. The risk avoided is that poor retrieval or missing source attribution can worsen rather than solve hallucinations. Confirm the outcome through: A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.

Incorrect Answers

B:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

C:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

D:

This choice addresses agent search prebuilt grounding versus custom rag; its main associated risk is: Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Question 662

A gen AI initiative reports: A generative answer provides a citation link that does not contain the asserted fact. How could the team verify that the intended business requirement is met?

  1. A cited result traces third-party data origin, access permission and refresh time for representative cases.
  2. A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.
  3. A time-sensitive public answer includes credible recent sources and states uncertainty when current evidence is missing.
  4. A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Explanation

Correct Answer

B:

The key acceptance evidence for A generative answer provides a citation link that does not contain the asserted fact is: A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim. It checks whether the team actually implemented: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness. A product configuration, demo, or persuasive response alone is not proof.

Incorrect Answers

A:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

C:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

D:

This choice addresses agent search prebuilt grounding versus custom rag; its main associated risk is: Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. However, the situation A generative answer provides a citation link that does not contain the asserted fact is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Question 663

A business team evaluating generative AI encounters this situation: An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which first change most directly addresses the observed failure?

  1. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  2. Adjust supported sampling, safety and response-length settings according to task goals, measuring quality rather than treating settings as factual guarantees.
  3. Integrate third-party content only through approved licensing, provenance and freshness controls rather than blindly trusting partner data.
  4. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.

Explanation

Correct Answer

A:

The decisive detail is: An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation. The appropriate business or technical decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness. The risk avoided is that poor retrieval or missing source attribution can worsen rather than solve hallucinations. Confirm the outcome through: A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.

Incorrect Answers

B:

This choice addresses sampling temperature top-p and token controls; its main associated risk is: Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

C:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

D:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Question 664

A business case reports: An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation. What risk should the decision-maker address before deployment?

  1. Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages.
  2. Poor retrieval or missing source attribution can worsen rather than solve hallucinations.
  3. Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers.
  4. Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records.

Explanation

Correct Answer

B:

This case exposes a specific failure mode: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. In the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation, the recommended response is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness. Independent evidence would be: A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.

Incorrect Answers

A:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

C:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

D:

This choice addresses sampling temperature top-p and token controls; its main associated risk is: Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records. However, the situation An organization assumes RAG automatically makes every sentence perfectly accurate without evaluation is primarily about rag retrieval and citations. The targeted decision is: Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Question 665

A business team evaluating generative AI encounters this situation: A company wants quick permission-aware search over a set of supported employee documents. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which response best meets the business need and its stated limits?

  1. Integrate third-party content only through approved licensing, provenance and freshness controls rather than blindly trusting partner data.
  2. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.
  3. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.
  4. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.

Explanation

Correct Answer

B:

The decisive detail is: A company wants quick permission-aware search over a set of supported employee documents. The appropriate business or technical decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. The risk avoided is that building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. Confirm the outcome through: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Incorrect Answers

A:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

C:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

D:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Question 666

A gen AI initiative reports: A company wants quick permission-aware search over a set of supported employee documents. Which measurement or trial should be required before approval?

  1. A time-sensitive public answer includes credible recent sources and states uncertainty when current evidence is missing.
  2. A seeded query retrieves the correct document chunk and an answer whose cited evidence supports its claim.
  3. A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.
  4. A cited result traces third-party data origin, access permission and refresh time for representative cases.

Explanation

Correct Answer

C:

The key acceptance evidence for A company wants quick permission-aware search over a set of supported employee documents is: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance. It checks whether the team actually implemented: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. A product configuration, demo, or persuasive response alone is not proof.

Incorrect Answers

A:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

B:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

D:

This choice addresses grounding with vetted third-party sources; its main associated risk is: Unverified external sources can introduce false claims, licensing problems or prompt injection into business answers. However, the situation A company wants quick permission-aware search over a set of supported employee documents is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Question 667

A business team evaluating generative AI encounters this situation: A developer writes custom embeddings code before testing a managed search configuration. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. What should the initiative prioritize to address the primary issue?

  1. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  2. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.
  3. Use permission-checked first-party content to ground answers about internal facts and expose reliable citations where appropriate.
  4. Adjust supported sampling, safety and response-length settings according to task goals, measuring quality rather than treating settings as factual guarantees.

Explanation

Correct Answer

B:

The decisive detail is: A developer writes custom embeddings code before testing a managed search configuration. The appropriate business or technical decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. The risk avoided is that building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. Confirm the outcome through: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Incorrect Answers

A:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

C:

This choice addresses grounding in first-party enterprise data; its main associated risk is: A model without current company documents may confidently invent policies, while broad retrieval may leak private content. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

D:

This choice addresses sampling temperature top-p and token controls; its main associated risk is: Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Question 668

A business case reports: A developer writes custom embeddings code before testing a managed search configuration. Why is this design likely to fail its intended business outcome?

  1. A model without current company documents may confidently invent policies, while broad retrieval may leak private content.
  2. Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records.
  3. Poor retrieval or missing source attribution can worsen rather than solve hallucinations.
  4. Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost.

Explanation

Correct Answer

D:

This case exposes a specific failure mode: Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. In the situation A developer writes custom embeddings code before testing a managed search configuration, the recommended response is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. Independent evidence would be: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Incorrect Answers

A:

This choice addresses grounding in first-party enterprise data; its main associated risk is: A model without current company documents may confidently invent policies, while broad retrieval may leak private content. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

B:

This choice addresses sampling temperature top-p and token controls; its main associated risk is: Higher randomness can reduce consistency, while aggressive output limits can truncate essential answers or structured records. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

C:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A developer writes custom embeddings code before testing a managed search configuration is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Question 669

A business team evaluating generative AI encounters this situation: A legal research task needs specialized citation and parsing not supported by a default setup. It must choose a solution that is useful, secure, properly sourced, and appropriate for its constraints. Which approach is most defensible under these requirements?

  1. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  2. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.
  3. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.
  4. Use permission-checked first-party content to ground answers about internal facts and expose reliable citations where appropriate.

Explanation

Correct Answer

C:

The decisive detail is: A legal research task needs specialized citation and parsing not supported by a default setup. The appropriate business or technical decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. The risk avoided is that building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost. Confirm the outcome through: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Incorrect Answers

A:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

B:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

D:

This choice addresses grounding in first-party enterprise data; its main associated risk is: A model without current company documents may confidently invent policies, while broad retrieval may leak private content. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

Question 670

The business case is: A legal research task needs specialized citation and parsing not supported by a default setup. Which TWO options together provide a suitable intervention and an independent acceptance check? (Select TWO.)

  1. A test answer cites the correct approved document and cannot retrieve another department's protected material.
  2. Use retrieval-augmented generation to select relevant source passages at request time, then evaluate relevance, permission and citation correctness.
  3. Use Google Search grounding when answers require recent public-world information, with source dates and scope checks.
  4. Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.
  5. A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance.

Explanation

Correct Answers

D:

The intervention for the stated situation A legal research task needs specialized citation and parsing not supported by a default setup is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work. It directly mitigates: Building a custom retriever without need or using prebuilt search for unsupported data formats can increase risk and cost.

E:

A separate outcome check for the situation A legal research task needs specialized citation and parsing not supported by a default setup is: A comparative retrieval pilot checks source support, citation quality, access, engineering effort and performance. It measures the effect of the selected intervention rather than relying on the existence of a proposal.

Incorrect Answers

A:

This choice addresses grounding in first-party enterprise data; its main associated risk is: A model without current company documents may confidently invent policies, while broad retrieval may leak private content. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

B:

This choice addresses rag retrieval and citations; its main associated risk is: Poor retrieval or missing source attribution can worsen rather than solve hallucinations. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.

C:

This choice addresses grounding with world data via google search; its main associated risk is: Public search is not proof of confidential enterprise records and can return unreliable or rapidly outdated pages. However, the situation A legal research task needs specialized citation and parsing not supported by a default setup is primarily about agent search prebuilt grounding versus custom rag. The targeted decision is: Choose prebuilt Agent Search grounding for managed retrieval needs and custom RAG APIs where specialized control justifies extra work.