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Google Generative-AI-Leader Exam Syllabus Topics:

TopicDetails
Topic 1
  • Techniques to Improve Generative AI Model Output: This section of the exam measures the skills of AI Engineers and focuses on improving model reliability and performance. It introduces best practices to address common foundation model limitations such as bias, hallucinations, and data dependency, using methods like retrieval-augmented generation, prompt engineering, and human-in-the-loop systems. Candidates are also tested on different prompting techniques, grounding approaches, and the ability to configure model settings such as temperature and token count to optimize results.
Topic 2
  • Fundamentals of Generative AI: This section of the exam measures the skills of AI Engineers and focuses on the foundational concepts of generative AI. It covers the basics of artificial intelligence, natural language processing, machine learning approaches, and the role of foundation models. Candidates are expected to understand the machine learning lifecycle, data quality, and the use of structured and unstructured data. The section also evaluates knowledge of business use cases such as text, image, code, and video generation, along with the ability to identify when and how to select the right model for specific organizational needs.
Topic 3
  • Business Strategies for a Successful Generative AI Solution: This section of the exam measures the skills of Cloud Architects and evaluates the ability to design, implement, and manage enterprise-level generative AI solutions. It covers the decision-making process for selecting the right solution, integrating AI into an organization, and measuring business impact. A strong emphasis is placed on secure AI practices, highlighting Google’s Secure AI Framework and cloud security tools, as well as the importance of responsible AI, including fairness, transparency, privacy, and accountability.
Topic 4
  • Google Cloud’s Generative AI Offerings: This section of the exam measures the skills of Cloud Architects and highlights Google Cloud’s strengths in generative AI. It emphasizes Google’s AI-first approach, enterprise-ready platform, and open ecosystem. Candidates will learn about Google’s AI infrastructure, including TPUs, GPUs, and data centers, and how the platform provides secure, scalable, and privacy-conscious solutions. The section also explores prebuilt AI tools such as Gemini, Workspace integrations, and Agentspace, while demonstrating how these offerings enhance customer experience and empower developers to build with Vertex AI, RAG capabilities, and agent tooling.

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
A marketing agency with a large digital asset library needs a Google Cloud solution to quickly and accurately search its digital files based on visual, spoken, or thematic content. What Google Cloud product should the agency use?

Answer: D

Explanation:
Google Cloud's Media Search (part of Vertex AI Search / Agent Search for media) is specialized for ingesting, indexing, and retrieving multimedia content-such as video, audio, and visual assets-by understanding spoken dialogue, visual actions, and semantic themes across unstructured digital libraries. Search for commerce and Vision API Product search are tailored to e-commerce product catalogs, while Document search focuses on text-heavy formats (PDFs, docs).


NEW QUESTION # 79
A global news agency is developing a generative AI tool to quickly summarize breaking news articles as they emerge online. The goal is to provide their audience with rapid updates on fast- developing stories from various global sources. What Google Cloud solution should they use?

Answer: A

Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.


NEW QUESTION # 80
A large online retailer with a vast product catalog wants to improve customer satisfaction by making it easier for shoppers to find the specific products they ' re looking for. The retailer also wants to provide personalized recommendations to increase sales. What should the company do?

Answer: C

Explanation:
AI Commerce Search on Gemini Enterprise for Customer Experience addresses both requirements: helping shoppers discover products through natural-language searches and delivering personalized recommendations that can increase conversions. It is purpose-built for commerce experiences and can interpret user intent, improve result relevance, and support individualized product discovery across large catalogs.
Recommendations alone addresses personalization but does not fully solve the natural-language product- search requirement. Vision API can identify and label image content, but image tagging by itself does not provide a complete commerce-search and recommendation experience. Agent Search on Gemini Enterprise Agent Platform is intended primarily for enterprise employees searching internal organizational information, not customers navigating a retail catalog. Because option C combines intelligent product search, personalized recommendations, and improved discovery within a commerce-focused offering, it is the most comprehensive solution.


NEW QUESTION # 81
A global travel booking platform named VistaVoyage is developing a generative AI system to identify payment fraud across about 45 million reservations each day. The team is concerned that adversaries may make small tweaks to inputs so the model incorrectly treats fraudulent behavior as legitimate. At what point in the machine learning lifecycle should robust protections against these adversarial tactics be established to preserve security?

Answer: D

Explanation:
Adversarial robustness needs to be designed into the model from the start and then sustained in production. During training you can harden models with adversarial training, robust data augmentation, regularization, and careful evaluation against adversarial and out of distribution test sets. In production you should continuously monitor for drift, anomalies, and suspicious input patterns and you should feed incidents back into retraining so the system improves over time.
This lifecycle approach ensures protections evolve with attacker tactics and with data and model changes.


NEW QUESTION # 82
A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?

Answer: A

Explanation:
The machine learning approach is determined by the nature of the data available and the desired output.
Data Available: Customer reviews (input) that are manually tagged with a sentiment category (output/label).
Desired Output: A model that can classify new, untagged reviews into one of the predefined categories (positive, negative, or neutral).
This scenario perfectly aligns with the definition of Supervised Learning (D). Supervised learning is the machine learning paradigm where the model is trained on a labeled dataset-a dataset where the input data is explicitly paired with the correct output label. The model learns a function that maps the input (the review text) to the output (the sentiment tag) and is then used to predict the label for unseen data.
Unsupervised Learning (B) is used for unlabeled data to find hidden patterns or groupings (clustering), which is not the goal here.
Reinforcement Learning (C) is used for training an agent through trial and error using a system of rewards and penalties.
Deep Learning (A) is a type of model (using deep neural networks) that can be used for supervised learning, but the learning approach required here is definitively supervised.
(Reference: Google's training materials on Machine Learning Approaches define Supervised Learning as training a model using labeled data to make predictions or classifications for new, unseen inputs. Sentiment analysis is a canonical example of a supervised learning classification task.)


NEW QUESTION # 83
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