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| Section | Objectives |
|---|---|
| Topic 1: Business Applications and Adoption Strategy | - Identifying business use cases for generative AI - Measuring ROI and value of generative AI initiatives - AI-driven transformation and workflow integration |
| Topic 2: Responsible AI and Governance | - Data privacy and security in generative AI systems - Responsible AI principles and compliance - AI safety, bias, and fairness considerations |
| Topic 3: Fundamentals of Generative AI | - Core concepts of generative AI and large language models - Key use cases and limitations of generative AI - Difference between traditional AI, machine learning, and generative AI |
| Topic 4: Google Cloud Generative AI Products and Tools | - Vertex AI and Gemini models overview - AI APIs and model deployment options on Google Cloud - Prompt design and prompt engineering tools |
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NEW QUESTION # 53
What is an example of unsupervised machine learning?
Answer: B
Explanation:
Unsupervised learning deals with unlabeled data. Identifying "natural groupings" or clusters in customer purchase patterns (e.g., segmenting customers into different buying behaviors without pre-defined labels) is a classic example of unsupervised learning (clustering). Options B, C, and D are examples of supervised learning, as they involve labeled data for training (product categories, renewal status, sales figures).
NEW QUESTION # 54
A company is using a language model to solve complex customer service inquiries. For a particular issue, the prompt includes the following instructions:
"To address this customer's problem, we should first identify the core issue they are experiencing. Then, we need to check if there are any known solutions or workarounds in our knowledge base. If a solution exists, we should clearly explain it to the customer. If not, we might need to escalate the issue to a specialist. Following these steps will help us provide a comprehensive and helpful response. Now, given the customer's message: 'My order hasn't arrived, and the tracking number shows no updates for a week,' what should be the next step in resolving this?" What type of prompting is this?
Answer: B
Explanation:
The prompt explicitly instructs the Large Language Model (LLM) to perform a step-by-step reasoning process before arriving at the final answer. The instructions lay out a sequential series of intermediate steps: "first identify," "then check," "if a solution exists, explain," "if not, escalate." This technique is known as Chain-of-Thought (CoT) Prompting. CoT is a powerful prompt engineering technique where the user or developer explicitly includes intermediate reasoning steps in the prompt. This guides the model to break down a complex, multi-step problem into smaller, manageable, logical steps, significantly improving its reasoning ability and the accuracy of its final output for complex queries like customer service troubleshooting or multi-step analysis.
Zero-shot (A) would be the raw question without any structure.
Few-shot (B) would involve providing examples of successfully solved problems.
Role-based (C) would involve assigning a persona (e.g., "Act as a customer service expert") but would not explicitly mandate the sequential process.
The inclusion of the explicit steps ("first identify," "then check," etc.) is the defining characteristic of Chain-of-Thought prompting.
(Reference: Google's courses on Prompt Engineering classify Chain-of-Thought prompting as the technique that improves reasoning by explicitly giving the model a series of sequential, intermediate steps to follow to arrive at a better answer for complex tasks.)
NEW QUESTION # 55
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: B
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 # 56
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: B
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.
________________________________________
NEW QUESTION # 57
A company's large learning model (LLM) is producing hallucinations that are a result of the Knowledge cutoff. How does retrieval-augmented generation (RAG) overcome this limitation?
Answer: A
Explanation:
The primary purpose of RAG is to address the "knowledge cutoff" and hallucination issues of LLMs. It does this by retrieving relevant, up-to-date information from external knowledge sources (like databases or documents) at inference time and then using this retrieved information to ground the LLM's generation, ensuring factual accuracy and relevance to the specific query.
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NEW QUESTION # 58
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