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NEW QUESTION # 14
A company is developing a generative AI application to analyze customer feedback collected through online surveys. Stakeholders are concerned about potential privacy risks associated with this data, as the feedback contains personally identifiable information (PII). They need to mitigate these risks before using the data to train the AI model. What action should the company prioritize?
Answer: A
Explanation:
The problem is the existence of Personally Identifiable Information (PII) within the customer feedback data, which introduces privacy risks for the development and training of the generative AI model. The goal is to mitigate these risks before using the data to train the AI model.
According to Google ' s Responsible AI and data handling best practices, when sensitive data like PII is present in a dataset intended for model training, the most critical step to prioritize is data minimization and privacy protection at the source. This is often achieved through anonymization or de-identification.
Applying data anonymization techniques (D) directly addresses the risk by removing or obscuring the sensitive data elements. This prevents the PII from being embedded into the model ' s parameters during training, thereby eliminating the risk of data leakage or privacy violations in the AI application ' s outputs.
This is a crucial early step in the ML lifecycle for datasets containing sensitive information.
Option C, implementing access controls, is a necessary security measure but is a reactive control that protects the raw data; it does not remove the PII risk from the derived model itself. Option A is a long-term change to data collection but doesn ' t solve the problem for the existing data. Option B relates to bias and accuracy, not specifically PII risk mitigation.
(Reference: Google Cloud ' s Secure AI Framework (SAIF) and Responsible AI principles emphasize protecting sensitive data at all stages of the ML lifecycle, with de-identification being the primary method before training.)
NEW QUESTION # 15
What are core hardware components of the infrastructure layer in the generative AI landscape?
Answer: D
Explanation:
The Generative AI landscape is often broken down into several functional layers: Applications, Agents, Platforms, Models, and Infrastructure.
The Infrastructure Layer is the foundation, providing the physical and virtual computing resources necessary to run and train the large models. These resources include servers, storage, networking, and most importantly, the specialized hardware accelerators required for high- volume, parallel computation.
The core hardware components are the Graphics Processing Units (GPUs) and the custom- designed Tensor Processing Units (TPUs) (A). These accelerators are optimized for the massive matrix operations fundamental to deep learning and Gen AI model training and inference.
NEW QUESTION # 16
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: B
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 # 17
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?
Answer: C
Explanation:
The limitation described is the AI model generating a false or misleading response (humans traveling faster than light is scientifically impossible/unproven) and presenting it as fact (confidently stating a fictional theory is real) without the ability to indicate its uncertainty or the source's fictional nature. This is the definition of a Hallucination in generative AI. AI Hallucinations occur when a Large Language Model (LLM) generates outputs that are factually incorrect, irrelevant, or nonsensical, despite being linguistically fluent and seemingly plausible. They arise because the model is designed to predict the most statistically probable next word or token based on its training data, even when it lacks information or when its training data contains a mixture of fact and fiction. The model is overconfident in its generated response, a behavior that diminishes user trust and reliability, especially in applications where factual accuracy is critical.
NEW QUESTION # 18
What is a definition of an AI agent?
Answer: D
Explanation:
In the generative AI landscape, an AI Agent is distinct from a basic chatbot or standard foundation model because of its ability to act autonomously to execute multi-step objectives.
The correct definition is an application that learns how to achieve a goal based on inputs and tools available to it (C). An agent is built around a core Large Language Model (LLM) which serves as its " brain.
" Given an objective or goal from a user, the agent uses a reasoning loop (such as ReAct) to evaluate inputs, break the goal into sub-tasks, and call external tools (like APIs, web search, databases, or calculators) to interact with the external world and accomplish the task.
* Option A is incorrect because agents are dynamic and action-oriented, not static.
* Option B describes a " Human-in-the-loop " supervisor, not the AI agent itself.
* Option D describes a chat interface or frontend wrapper, which is merely a way to communicate with an agent, not the definition of the agent ' s functional architecture.
(Reference: Google Cloud ' s official architecture guides for Generative AI define an Agent as an autonomous software entity driven by an LLM that accepts natural language goals, breaks them down into executable workflows, and leverages tools to alter states or retrieve information to satisfy that goal.)
NEW QUESTION # 19
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