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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified - Generative AI Leader Exam |
| Exam Number: | GCP-GAIL |
| Exam Price: | USD 99.00 |
| Passing Score: | Pass / Fail (Approx 70%) |
| Related Certifications: | Google Cloud Certified - Generative AI Leader |
| Exam Format: | Multiple choice questions with single or multiple correct answers |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Real Exam Qty: | 50-60 |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Remote as well as onsite |
| Pre Condition: | This certification is for anyone in any job role, with or without hands-on technical experience. |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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NEW QUESTION # 31
A travel app asks users to take a photo of a famous landmark and then returns a written overview with historical notes and nearby attractions. The system's capability to interpret the picture and produce natural language output reflects what kind of model?
Answer: C
Explanation:
This scenario requires understanding visual content from a photo and then generating a textual explanation. That means the system consumes one modality as an image and produces another modality as text. This cross-modality capability is exactly what a multimodal approach provides, since it jointly handles vision and language to produce coherent natural language output based on visual input.
NEW QUESTION # 32
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 However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles.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: C
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for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language).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.
NEW QUESTION # 33
A customer service team wants to use generative AI to improve the quality and consistency of their email responses to customer inquiries. They need a solution that can guide the AI to adopt a helpful, empathetic tone while adhering to company policies. Which prompting technique should they use?
Answer: A
Explanation:
The most direct and effective way to influence the style, personality, and knowledge context of an AI ' s response is through Role Prompting.
Role Prompting involves instructing the model to assume a specific persona (a " role " ) before responding.
By assigning the AI the role of an " experienced customer service representative " (B), the model is implicitly directed to adopt a professional, helpful, and empathetic tone. Furthermore, specifying " with corporate knowledge " directs the model to prioritize responses consistent with internal company policies. This technique is a foundational element of prompt engineering, often used in conjunction with other methods (like grounding, if specific policy documents were needed) to dramatically shift the output style and relevance.
While Few-shot prompting (D) could provide examples to influence style, it ' s less efficient than a clear role instruction and still requires the model to infer the persona. Prompt Chaining (A) is used to manage multi-turn conversation memory, not to set the tone or persona. Therefore, defining the Role is the core technique for establishing both the desired tone and the necessary professional context in a single instruction.
(Reference: Google ' s documentation on prompt engineering for customer service shows examples where users begin the prompt with " I am a customer service representative " to set the tone and persona for the generated response, confirming Role Prompting as the technique for ensuring style and consistency.)
NEW QUESTION # 34
During an annual strategy briefing at Meadowbrook Supply, the chief executive outlines several intelligent initiatives. She describes a model that forecasts customer churn from past behavior.
She mentions a conversational agent that writes personalized promotional emails. She explains autonomous systems that improve stocking layouts in regional warehouses. She also notes detectors that flag suspicious payment activity. What is the most accurate umbrella term that she should use to collectively describe these capabilities?
Answer: A
Explanation:
This is the most accurate umbrella term because it collectively covers predictive modeling of churn, conversational systems that generate personalized messages, autonomous decision making for warehouse layouts, and anomaly detection for payments.
This umbrella includes systems that learn from data and make predictions about customer behavior. It also includes conversational agents that produce tailored content and autonomous agents that optimize actions in real environments. It further includes detectors that identify unusual or risky transactions. All of these capabilities fall within the broader field of intelligent systems.
NEW QUESTION # 35
A marketing team wants to use a foundation model to create social media and advertising campaigns. They want to create written articles and images from text. They lack deep AI expertiseand need a versatile solution.
Which Google foundation model should they use?
Answer: A
Explanation:
Gemini is Google's most advanced and multimodal foundation model, capable of understanding and generating various forms of content, including text and images, from a single prompt. Its versatility makes it suitable for marketing teams that need to create diverse campaign materials without deep AI expertise.
Imagen is specifically for image generation, Gemma is a family of smaller, open models, and Veo is for video generation.
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NEW QUESTION # 36
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