GENERATIVE-AI-LEADER VALID BRAINDUMPS EBOOK - GENERATIVE-AI-LEADER INSTANT ACCESS

Generative-AI-Leader Valid Braindumps Ebook - Generative-AI-Leader Instant Access

Generative-AI-Leader Valid Braindumps Ebook - Generative-AI-Leader Instant Access

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Google Cloud Certified - Generative AI Leader Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
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. Deep learning
  • B. Unsupervised learning
  • C. Reinforcement learning
  • D. Supervised learning

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 # 39
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 collaborative AI community and support forums connect developers with AI experts.
  • C. Google Cloud's focus on continuous improvement provides access to the latest AI tools, features, and best practices.
  • D. Google Cloud's pre-trained models and low- and no-code AI tools and services.

Answer: D

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.
________________________________________


NEW QUESTION # 40
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. Vision AI
  • B. Document AI API
  • C. Dataflow
  • D. Natural Language API

Answer: B

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.
________________________________________


NEW QUESTION # 41
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. Use prompt engineering techniques, like few-shot prompting, to provide the chatbot with examples of successful interactions.
  • B. Lower model temperature setting to produce more consistent and predictable responses.
  • C. Use strict keyword matching to ensure that the chatbot only responds to specific commands.
  • D. Limit the chatbot's training data to prevent it from learning irrelevant information.

Answer: A

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 # 42
A global news agency is developing a generative AI tool to quickly summarize breaking newsarticles as they emerge online. The goal is to provide their audience with rapid updates on fast-developing stories from various global sources. What Google Cloud solution should they use?

  • A. Grounding with Google Search
  • B. Vertex AI Natural Language API
  • C. BigQuery
  • D. Document AI

Answer: A

Explanation:
For summarizing breaking news articles as they emerge online from various global sources, the generative AI model needs access to current, broad, and rapidly updating information. Grounding with Google Search allows the LLM to pull in the latest information from the web, ensuring the summaries are current and comprehensive. While Vertex AI Natural Language API can summarize text, it wouldn't inherently have access to the latest breaking news unless explicitly fed.
________________________________________


NEW QUESTION # 43
......

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