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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader Certification Exam |
| Exam Number: | Generative-AI-Leader |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple choice |
| Exam Price: | USD 99 (plus tax where applicable) |
| Passing Score: | Not publicly disclosed |
| Exam Duration: | 90 minutes |
| Available Languages: | English, Japanese, Portuguese, Spanish |
| Real Exam Qty: | 50-60 |
| Recommended Training: | Generative AI Leader Study Guide Generative AI Leader Training Course |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No prerequisites required; open to all roles and backgrounds |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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NEW QUESTION # 94
A company is developing an AI character for a video game. The AI character needs to learn how to navigate a complex environment and make decisions to achieve certain objectives within the game. When the AI takes actions that lead to positive outcomes, like finding a reward or overcoming an obstacle, it receives a positive score. When it takes actions that lead to negative outcomes, like hitting a wall or losing progress, it receives a negative score. Through this process of trial and error, the AI gradually improves the character's ability to play the game effectively. What machine learning should the company use?
Answer: A
Explanation:
This scenario perfectly describes reinforcement learning. In reinforcement learning, an agent learns to make decisions by interacting with an environment, receiving1 rewards for desirable actions and penalties for undesirable ones,2 and iteratively improving its behavior through trial and error to maximize cumulative reward.
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NEW QUESTION # 95
A logistics company wants to use a generative AI (gen AI) agent to automatically check real-time inventory levels across its warehouses and adjust delivery schedules. The gen AI agent needs access to internal inventory dat
Answer: B
Explanation:
To achieve real-time inventory checks and adjust delivery schedules, the generative AI agent needs live access to the company's internal inventory data. Google Cloud databases provide the structured storage for this data, and Vertex AI offers the platform to build, deploy, and manage the AI agent, including connecting it to these live data sources. This approach allows the agent to make informed decisions based on current information. Building a custom API for every interaction might be less cost-effective in the long run for dynamic inventory data. Pre-built chatbots might not have the direct integration needed for real-time adjustments, and fine-tuning with sample data wouldn't provide the live data access required.
NEW QUESTION # 96
A team is discussing the different layers of the generative AI (gen AI) landscape and where specific tools and technologies fit within those layers. They want to clarify the role of Gemini Enterprise Agent Platform and data management tools in the overall landscape. In which layer of the generative AI landscape are Gemini Enterprise Agent Platform and data management tools?
Answer: C
Explanation:
The generative AI technology stack consists of four key layers:
Infrastructure: Compute (TPUs, GPUs, Compute Engine), networking, and physical infrastructure.
Models: Foundation models (Gemini, Gemma, Imagen, third-party models in Model Garden).
Platform: Development and management software suites-including Gemini Enterprise Agent Platform (formerly Vertex AI), Vertex AI Studio, Model Registry, and data management/grounding services-used to build, tune, evaluate, and orchestrate workloads.
Agents / Applications: End-user conversational agents, copilots, and business applications delivering specific capabilities.
NEW QUESTION # 97
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 # 98
A data science team needs a centralized and organized location to store its various model versions, track their metadata, and easily deploy them to the respective applications. What Google Cloud service should they use?
Answer: D
Explanation:
A Model Registry (specifically part of Vertex AI Model Registry) is designed precisely for managing the lifecycle of machine learning models. It provides a centralized repository for storing, versioning, tracking metadata, and facilitating the deployment of models, which is essential for MLOps. Cloud Storage is for raw data, BigQuery for data warehousing, and Vertex AI Pipelines for workflow orchestration.
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NEW QUESTION # 99
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