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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Core concepts of generative AI and large language models - Key use cases and limitations of generative AI - Difference between traditional AI, machine learning, and generative AI |
| Responsible AI and Governance | - Data privacy and security in generative AI systems - AI safety, bias, and fairness considerations - Responsible AI principles and compliance |
| Google Cloud Generative AI Products and Tools | - Prompt design and prompt engineering tools - Vertex AI and Gemini models overview - AI APIs and model deployment options on Google Cloud |
| Business Applications and Adoption Strategy | - Identifying business use cases for generative AI - AI-driven transformation and workflow integration - Measuring ROI and value of generative AI initiatives |
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NEW QUESTION # 52
Sundale Electronics launched a generative AI support assistant, and after going live they observe that the assistant often produces fluent responses that fail to address customers' questions about their newly introduced smart thermostats. The model was trained on a large set of generic support logs collected over the past six years, and that set contains very little information about the latest devices. Which data quality attribute is most likely deficient and causing these off target replies?
Answer: B
Explanation:
The correct option is Relevance because the training data does not adequately cover the new smart thermostats so the model generates fluent responses that are not aligned with the users questions.
Relevance measures how well the data used matches the target task and information needs.
Since the dataset consists mostly of older generic support logs and contains little content about the newest devices, the model lacks pertinent examples. This misalignment leads to answers that sound good but do not address the specific queries about the latest thermostats.
NEW QUESTION # 53
A team is using a generative AI model to automatically generate short summaries of customer feedback. They need to ensure that these summaries are concise and easy to digest. What model setting should they adjust?
Answer: D
Explanation:
The objective is to make the generated summaries concise-that is, to control their length.
In the configuration of a generative AI model, particularly a large language model (LLM), the parameter used to directly control the maximum size of the response is the Output Length parameter (often referred to as max_output_tokens or max_tokens). By setting a low limit on this parameter, the team can ensure that the model is forced to terminate its response once that limit is reached, resulting in a shorter, more concise summary that is " easy to digest, " as requested.
The other parameters control different aspects of the output quality:
Temperature (C) controls the creativity or randomness of the output. Lowering it makes the output more predictable; raising it makes it more diverse. It does not control length.
Top-p (A) is a decoding method related to temperature that also controls the model ' s creativity by limiting the vocabulary from which it can choose the next token. It does not control length.
Safety settings (B) are used to filter and block the generation of harmful, illegal, or inappropriate content.
They do not affect the length or conciseness of the output.
(Reference: Google Cloud ' s Generative AI documentation on model parameters explicitly lists max_output_tokens or Output Length as the setting used to determine the maximum size of a model ' s generated response.)
NEW QUESTION # 54
A global news company is using a large language model to automatically generate summaries of news articles for their website. The model's summary of an international summit was accurate until it hallucinated by stating a detail that did not occur. How should the company overcome this hallucination?
Answer: D
Explanation:
The core problem is the model's hallucination--it invented a factual detail--in a context (news reporting) where factual accuracy is non-negotiable. To correct a factual error in a generative summary, the model must be constrained to speak only based on verifiable facts from a reliable source.
The most effective technique to combat hallucinations and ensure factual adherence is Grounding (D). Grounding connects the Large Language Model's (LLM's) output to a specific, trusted, and verifiable source of information. This is often implemented using Retrieval- Augmented Generation (RAG). In this scenario, grounding the summary model on the original source articles ensures that every generated statement is directly entailed by the provided facts (the source article content).
NEW QUESTION # 55
A boutique sneaker label plans to use generative AI to render concept images for upcoming footwear lines. They can choose between a compact and carefully curated set of about 15,000 images of their own past collections and a very large and mixed set of roughly 2.2 million generic footwear photos from public sources. If they prioritize the smaller curated set of their proprietary designs as the primary training data, what outcome should they expect from the resulting model?
Answer: C
Explanation:
Prioritizing a smaller but curated set of proprietary images makes the model learn the label's distinctive patterns, motifs, and color relationships more strongly. The training signal concentrates on consistent brand examples, so the generator better reproduces the brand's aesthetic and design language. Because the data distribution is narrower than a large mixed corpus, the model tends to explore fewer directions and may trade off breadth of styles for fidelity to the brand identity.
NEW QUESTION # 56
An engineering team at example.com spends about 12 hours each week producing boilerplate and scaffolding for routine service endpoints, and they want an AI capability that can generate this repetitive code from concise requirements or from established patterns so the developers can concentrate on complex tasks. What primary use of generative AI does this scenario represent?
Answer: C
Explanation:
The scenario describes generating boilerplate and scaffolding from concise requirements or established patterns so developers can focus on more complex work.
This use case aligns with generative AI that produces source code from prompts or patterns. It automates repetitive implementation details, accelerates service setup, and helps teams reduce time spent on routine endpoint creation while maintaining consistency.
NEW QUESTION # 57
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