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Google Generative-AI-Leader Exam Syllabus Topics:
Topic
Details
Topic 1
- Creating AV Solutions: This section of the exam measures the skills of an AV Systems Designer and covers the processes involved in understanding client requirements and translating them into practical AV solutions. It includes tasks such as conducting customer needs analysis, performing site surveys to evaluate conditions like lighting and acoustics, developing AV project scopes, and designing system layouts and documentation.
Topic 2
- Supporting AV System Operation: This section of the exam measures the skills of an AV Support Specialist and focuses on providing assistance in operating audiovisual systems. It includes remote and onsite troubleshooting, training users, and delivering live event support, ensuring systems function effectively in real-world usage scenarios.
Topic 3
- Servicing AV Solutions: This section of the exam measures skills of an AV Maintenance Technician and focuses on maintaining and repairing AV systems. Tasks include supervising operations, conducting routine maintenance such as firmware updates and component replacements, and resolving issues through troubleshooting and repair processes, ensuring long-term system performance.
Topic 4
- Implementing AV Solutions: This section of the exam measures the skills of an AV Integration Technician and focuses on turning AV system designs into reality. It assesses the ability to integrate systems by verifying components and managing supply installations, as well as handling documentation, training, and creating as-built schematics that support ongoing system operation.
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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q21-Q26):
NEW QUESTION # 21
An organization wants to use generative AI to create a marketing campaign. They need to ensure that the AI model generates text that is appropriate for the target audience. What should the organization do?
- A. Adjust the temperature parameter.
- B. Use prompt chaining.
- C. Use role prompting.
- D. Use few-shot prompting.
Answer: C
Explanation:
Role prompting is a technique where you instruct the generative AI model to "act as" a specific persona or character. By assigning the model a role (e.g., "Act as a marketing expert writing for a young, tech-savvy audience"), you can guide its tone, style, and content to be appropriate for the target audience of the marketing campaign.
________________________________________
NEW QUESTION # 22
A company is developing a conversational AI chatbot. They need to ensure the chatbot can engage in human- like conversations and provide accurate information. What should they do to enhance thechatbot's ability to understand and respond effectively to user prompts?
- A. Limit the chatbot's training data to prevent it from learning irrelevant information.
- B. Lower model temperature setting to produce more consistent and predictable responses.
- C. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
- D. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
Answer: C
Explanation:
Prompt engineering, especially techniques like few-shot prompting (providing examples of desired input- output pairs), is crucial for guiding a generative AI model to understand context and generate relevant, human- like responses. Limiting data or using strict keyword matching would severely restrict the chatbot's conversational ability, and lowering temperature makes responses less creative, not necessarily more understanding.
________________________________________
NEW QUESTION # 23
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?
- A. Unsupervised learning
- B. Supervised learning
- C. Reinforcement learning
- D. Deep learning
Answer: A
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 # 24
A company wants to choose a generative AI (gen AI) use case that will be successful and have the most impact. What key factor should they determine first according to Google Cloud-recommended practices?
- A. The number of employees who will be trained to use the new gen AI tools.
- B. The frequency of updates to the underlying foundation models used by different gen AI platforms.
- C. The availability of pre-trained models that are offered on various cloud computing platforms.
- D. The specific business problems the company aims to solve and the desired outcomes.
Answer: D
Explanation:
A fundamental principle for successful AI adoption, including generative AI, is to start with clear business problems and desired outcomes. Without a well-defined problem, the AI solution might not deliver meaningful value, regardless of the technology used. This "problem-first" approach is crucial for impactful AI strategy.
________________________________________
NEW QUESTION # 25
A company wants to adopt generative AI and is concerned about vendor lock-in. They want to maintain flexibility in their technology stack. What Google Cloud strength would ease their concerns?
- A. Google Cloud's focus on automation aims to replace human jobs with AI systems, potentially leading to significant workforce reductions.
- B. Google Cloud's AI solutions have an open approach that supports customer choice across offerings.
- C. Google Cloud's strict adherence to proprietary technologies ensures the highest level of security and performance.
- D. Google Cloud's AI solutions are pre-packaged for easy deployment, eliminating the need for customization and integration efforts.
Answer: B
Explanation:
Google Cloud promotes an open and flexible approach to its AI offerings, supporting open standards, open- source initiatives (like TensorFlow, Kubernetes, and Gemma), and providing various integration options. This helps alleviate vendor lock-in concerns by giving customers choice and control over their technology stack.
________________________________________
NEW QUESTION # 26
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