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| Section | Objectives |
|---|---|
| Google Cloud Generative AI Products and Tools | - AI APIs and model deployment options on Google Cloud - Vertex AI and Gemini models overview - Prompt design and prompt engineering tools |
| Responsible AI and Governance | - Data privacy and security in generative AI systems - AI safety, bias, and fairness considerations - Responsible AI principles and compliance |
| Business Applications and Adoption Strategy | - Measuring ROI and value of generative AI initiatives - Identifying business use cases for generative AI - AI-driven transformation and workflow integration |
| Fundamentals of Generative AI | - Difference between traditional AI, machine learning, and generative AI - Key use cases and limitations of generative AI - Core concepts of generative AI and large language models |
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NEW QUESTION # 42
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: B
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 # 43
An organization wants to use generative AI to create a chatbot that can answer customer questions about their account balances. They need to ensure that the chatbot can access previous portions of the conversation with the customer. Which prompting technique should they use?
Answer: B
Explanation:
Prompt chaining (or conversational memory/context management) is the technique used to maintain the conversational context. It involves feeding previous turns of a conversation (or a summary of them) back into the model along with the current user query, allowing the chatbot to
"remember" and reference past interactions for coherent and contextually relevant responses, especially crucial for tasks like checking account balances that span multiple turns.
NEW QUESTION # 44
What is a key advantage of using Google's custom-designed TPUs?
Answer: D
Explanation:
TPUs (Tensor Processing Units) are custom-designed hardware accelerators developed by Google specifically for high-performance machine learning tasks. Their advantage lies in their architecture, which is optimized for the massively parallel matrix multiplication operations that form the mathematical backbone of deep learning and large language models (LLMs).
TPUs excel at parallel processing (C) for training and running machine learning workloads, allowing computations to be performed simultaneously across numerous cores. This makes them significantly faster and more efficient than traditional CPUs or even general-purpose GPUs for tasks like training massive generative models (e.g., Gemini).
TPUs are a core component of the Infrastructure Layer in the Generative AI landscape, providing the foundational compute resources.
While Google offers very small, specialized TPUs for the edge (like Edge TPU), the primary, large-scale advantage is in the cloud for accelerating training and inference for complex ML models.
Options A describes the Edge TPU or Gemini Nano deployment strategy, not the general, key advantage. Options B and D misrepresent the function, as TPUs are compute hardware, not storage accelerators or general-purpose CPU replacements.
(Reference: Google's training materials on the Generative AI Infrastructure Layer explicitly list TPUs and GPUs as the physical hardware components providing the core computing resources needed for generative AI, with TPUs being specialized for accelerating ML workloads and parallel processing.)
NEW QUESTION # 45
What is the definition of generative AI?
Answer: C
Explanation:
The defining characteristic of generative AI is its ability to create new, original content that resembles its training data. This includes various modalities like text, images, music, and code, rather than just classifying, predicting, or analyzing existing data.
NEW QUESTION # 46
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: A
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 # 47
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