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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Generative AI Leader Certification Exam |
| Exam Number: | Generative-AI-Leader |
| Real Exam Qty: | 50-60 |
| Certificate Validity Period: | 3 years |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple choice |
| Available Languages: | Spanish, English, Portuguese, Japanese |
| Passing Score: | Not publicly disclosed |
| Exam Price: | USD 99 (plus tax where applicable) |
| Recommended Training: | Generative AI Leader Training Course Generative AI Leader Study Guide |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Generative-AI-Leader Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No prerequisites required; open to all roles and backgrounds |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/generative-ai-leader |
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NEW QUESTION # 15
A company wants a generative AI platform that provides the infrastructure, tools, and pre-trained models needed to build, deploy, and manage its generative AI solutions. Which Google Cloud offering should the company use?
Answer: B
Explanation:
Vertex AI is Google Cloud's unified machine learning platform that provides end-to-end support for the ML lifecycle, including access to pre-trained models (foundation models), tools for fine- tuning, deployment, and management of generative AI solutions. BigQuery is a data warehouse, GKE is for container orchestration, and Cloud Storage is for object storage; while they might be components used with Vertex AI, they are not the comprehensive generative AI platform themselves.
NEW QUESTION # 16
A company has a machine learning project that involves diverse data types like streaming data and structured databases. How does Google Cloud support data gathering for this project?
Answer: D
Explanation:
Google Cloud offers a comprehensive suite of services for data ingestion and storage. Pub/Sub is for streaming data, Cloud Storage for various file types (including unstructured), and Cloud SQL for relational structured databases. These are fundamental for gathering diverse data. Gemini is a model, BigQuery is for analysis, and Vertex AI is for ML platform, not primary data collection tools themselves.
NEW QUESTION # 17
A finance team wants to use Gemma to help with daily tasks so that the financial analysts can focus on other work. Which business problem can Gemma most efficiently address?
Answer: C
Explanation:
Gemma is a family of lightweight, open-source Large Language Models (LLMs) from Google that are based on the same research and technology as the Gemini models. As an LLM, its core strength lies in language- based tasks, particularly the generation and summarization of text.
The problem that Gemma, or any pure LLM, can most efficiently address is:
Generating text: creating new content quickly (Option D).
Summarizing text: condensing long communications or documents (Option D).
Option D, producing high-quality written summaries and initial drafts, is a natural language generation task that aligns perfectly with the core function of an LLM like Gemma. It is a key productivity booster for analysts needing to draft reports or emails quickly.
Option B (Analyzing large datasets/predicting performance) requires traditional machine learning (ML) models or analytical tools like BigQuery ML, as LLMs are not specialized for numerical predictive modeling.
Option C (Extracting key financial figures from documents) is a task for a highly specialized tool like Google
' s Document AI.
Option A (Building internal knowledge bases for Q & A) is a broader use case that is best solved with a platform solution using RAG, such as Vertex AI Search, not just a base model.
(Reference: Google ' s description of the Gemma model family emphasizes its role as a flexible, open LLM that excels at language fundamentals, making it ideal for content creation, summarization, and other text generation tasks.)
NEW QUESTION # 18
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: C
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 # 19
A financial services company receives a high volume of loan applications daily submitted as scanned documents and PDFs with varying layouts. The manual process of extracting key information is time- consuming and prone to errors. This causes delays in loan processing and impacts customer satisfaction. The company wants to automate the extraction of this critical data to improve efficiency and accuracy. Which Google Cloud tool should they use?
Answer: C
Explanation:
Document AI API is specifically designed for intelligent document processing. It uses machine learning to extract structured data from unstructured documents like scanned forms and PDFs, even with varying layouts.
This directly addresses the challenge of automating data extraction from loan applications. Natural Language API focuses on text understanding, Vision AI on image analysis (not structured extraction from documents), and Dataflow is for data processing pipelines.
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NEW QUESTION # 20
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