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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Business Strategies for Successful Generative AI Solutions | 15% | - Governance, risk management, and compliance - Planning and adoption frameworks - Identifying business use cases and value opportunities - Scaling and measuring success of generative AI initiatives |
| Topic 2: Google Cloud's Generative AI Offerings | 35% | - Enterprise integration and security features - Overview of Google Cloud generative AI services and tools - Vertex AI generative AI capabilities - Model Garden and available models - Generative AI application development platforms |
| Topic 3: Techniques to Improve Generative AI Model Output | 20% | - Prompt engineering principles and best practices - Mitigation of bias and inaccuracies - Fine-tuning and adaptation methods - Evaluation and optimization of output quality |
| Topic 4: Fundamentals of Generative AI | 30% | - Key technologies and differences from traditional AI - Foundation models: definition, capabilities, and use cases - Responsible AI principles and application - Core concepts and characteristics of generative AI |
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NEW QUESTION # 111
SummitCart, a global e commerce fulfillment company, is deploying a generative AI driven system in its regional distribution centers to observe conveyor operations and forecast sorter and motor failures in real time. Any outage would pause order packing and could cost several million dollars per hour. When choosing the model and the managed platform, which characteristic should be prioritized for this mission critical rollout?
Answer: A
Explanation:
This rollout is mission critical and any downtime would incur enormous costs, so the platform and model selection must prioritize guaranteed uptime.
For an always-on operational system in distribution centers you need high availability commitments that are explicit and enforceable. A documented uptime SLA signals that the provider designs and operates the service for reliability and that it will be supported with measurable objectives and remediation if targets are missed. Choosing services that publish clear availability targets and provide regional resilience, failover capabilities, and enterprise support reduces the risk of production outages and protects revenue.
NEW QUESTION # 112
A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?
Answer: C
Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify
"anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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NEW QUESTION # 113
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: D
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 # 114
The office of the CISO wants to use generative AI (gen AI) to help automate tasks like summarizing case information, researching threats, and taking actions like creating detection rules. What agent should they use?
Answer: D
Explanation:
Given the tasks involve researching threats and creating detection rules, the most appropriate and specialized agent would be a Security agent. This type of agent would be pre-configured or easily adaptable to understand security-specific contexts, data, and actions within a CISO's domain.
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NEW QUESTION # 115
A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
Answer: A
Explanation:
The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles.
This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups.
Hallucination (C) is the invention of facts or data.
Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics.
Bias is the result of the data dependency, not the fundamental limitation itself.
(Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
NEW QUESTION # 116
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