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New Generative-AI-Leader Test Questions & Generative-AI-Leader Visual Cert Test

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
A company's development team is eager to start building generative AI solutions with Google Cloud, but has limited experience in AI development. They need to launch their gen AI solution quickly. What Google Cloud benefit would help the company achieve their goal?

A. Google Cloud's comprehensive training materials and tutorials to help developers.
B. Google Cloud's pre-trained models and low- and no-code AI tools and services.
C. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
D. Google Cloud's collaborative AI community and support forums connect developers with AI experts.

Answer: B

Explanation:
For a team with limited AI experience needing to launch quickly, leveraging pre-trained models (foundation models) and low-code/no-code tools significantly reduces the development burden and accelerates time to market. This allows them to build and deploy generative AI solutions without requiring deep expertise from scratch. While other options are helpful, this directly addresses the need for quick launch with limited experience.
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NEW QUESTION # 32
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?

A. Document AI API
B. Natural Language API
C. Vision AI
D. Dataflow

Answer: A

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 # 33
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?

A. Temperature
B. Top-p (nucleus sampling)
C. Safety settings
D. Output length

Answer: D

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 # 34
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 expertise and need a versatile solution. Which Google foundation model should they use?

A. Veo
B. Gemini
C. Imagen
D. Gemma

Answer: B

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 # 35
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?

A. Google Cloud relies on Vertex AI to connect to external data.
B. Google Cloud provides tools such as Pub/Sub, Cloud Storage, and Cloud SQL.
C. The Gemini app is the primary Google Cloud tool for directly collecting data.
D. Google Cloud's strengths are in the data analysis tools such as BigQuery.

Answer: B

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 # 36
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