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Google Generative-AI-Leader Exam Syllabus Topics:

SectionWeightObjectives
Google Cloud's generative AI offerings35%- Describe Google Cloud's gen AI product and service portfolio.
  • 1. Gemini for Google Cloud
  • 2. Vertex AI Studio
  • 3. Vertex AI
  • 4. Google Workspace
  • 5. Model Garden
- Identify the use cases and strengths of Google's foundation models.
  • 1. Gemma
  • 2. Veo
  • 3. Gemini
  • 4. Imagen
Fundamentals of generative AI30%- Identify the core layers of the gen AI landscape and the business implications.
  • 1. Applications
  • 2. Platforms
  • 3. Models
  • 4. Agents
  • 5. Infrastructure
- Describe how various data types are used in gen AI and the business implications.
  • 1. Explaining the characteristics and importance of data quality and data accessibility in AI (e.g., completeness, consistency, relevance, availability, cost, format)
  • 2. Identifying the differences between labeled and unlabeled data
  • 3. Identifying the differences between structured and unstructured data, and identifying real world examples of each type
- Describe core generative AI (gen AI) concepts and use cases.
  • 1. Identifying how to choose the appropriate foundation model for a business use case (e.g., modality, context window, security, availability and reliability, cost)
  • 2. Identifying the stages of the machine learning lifecycle (e.g., data ingestion, data preparation, model training, model deployment, model management) and the Google Cloud tools for each stage
  • 3. Defining core gen AI concepts (e.g., artificial intelligence, natural language processing, machine learning, generative AI, foundation models, multimodal foundation models, diffusion models, prompt tuning, prompt engineering, large language models)
  • 4. Describing the machine learning approaches (e.g., supervised, unsupervised, reinforcement)
Techniques to improve gen AI model output20%- Describe prompt engineering techniques and their purpose.
  • 1. One-shot
  • 2. Zero-shot
  • 3. Few-shot
  • 4. Chain of thought
- Describe the process of fine-tuning gen AI models.
  • 1. Reinforcement learning from human feedback (RLHF)
  • 2. Supervised tuning
- Describe how grounding can be used to improve model output.
  • 1. Grounding with enterprise data
  • 2. Grounding with Google Search
Business strategies for a successful gen AI solution15%- Describe change management best practices and their importance.
  • 1. Creating a culture of innovation
  • 2. Enabling AI adoption
- Describe Google's approach to responsible AI and its importance.
  • 1. Responsible AI best practices
  • 2. Google's AI principles
- Describe best practices for a successful gen AI project.
  • 1. Evaluating AI solutions
  • 2. Choosing the right model
  • 3. Building a business case

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
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


NEW QUESTION # 82
A customer success manager at BrightWave Systems uses the Gemini app. They want Gemini to always remember their role as "Customer Success Manager at BrightWave Systems" and to consistently apply the company's standard account tiers and playbooks for everyday conversations so they do not need to restate this in every chat. Separately they want a dedicated assistant for preparing quarterly business reviews that is preloaded with their slide templates, a persuasive yet consultative tone, and knowledge of the current marketing initiatives. Which Gemini capabilities should they use for the persistent general context and for the specialized task assistant?

Answer: D

Explanation:
Saved Info is designed to hold persistent details about you and your preferences so it can remember your role and your company's standard tiers and playbooks across chats. This allows the customer success manager to avoid retyping their identity and common defaults in every new conversation and ensures consistency in everyday interactions.
A Gem is a customizable assistant for a focused job. Creating one for quarterly business reviews lets you preload slide templates, set a persuasive yet consultative tone, and include the latest marketing initiatives so the assistant is specialized for QBR preparation while remaining separate from general day to day chats.


NEW QUESTION # 83
A market research firm wants to use a Google Cloud prebuilt generative AI offering to streamline the process of extracting and synthesizing information from lengthy market reports and research papers. Their goal is to improve efficiency and provide faster insights to their clients. What should the organization do?

Answer: D

Explanation:
NotebookLM is a prebuilt, source-grounded research and synthesis tool designed for working with uploaded documents and other selected sources. The firm can provide its market reports and research papers, ask questions about their contents, generate summaries, compare information, and identify important themes.
Because responses are grounded in the supplied sources, researchers can obtain focused insights more efficiently while retaining the ability to review supporting material. The standard Gemini app can provide general assistance but is less specifically optimized for a defined document collection. Building custom conversational agents would introduce unnecessary development effort when a prebuilt offering satisfies the requirement. Gemini in Google Workspace can assist with drafting and collaboration, but the central need is extracting and synthesizing information from lengthy source documents. NotebookLM is therefore the most appropriate choice.


NEW QUESTION # 84
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 # 85
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 # 86
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