Free PDF Quiz 2025 Amazon AIF-C01–Efficient Reliable Exam Labs
Free PDF Quiz 2025 Amazon AIF-C01–Efficient Reliable Exam Labs
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Amazon AIF-C01 Exam Syllabus Topics:
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Amazon AWS Certified AI Practitioner Sample Questions (Q55-Q60):
NEW QUESTION # 55
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?
- A. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
- B. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
- C. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
- D. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
Answer: C
NEW QUESTION # 56
A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix.
Which solution scope gives the company the MOST ownership of security responsibilities?
- A. Using a third-party enterprise application that has embedded generative AI features.
- B. Building an application by using an existing third-party generative AI foundation model (FM).
- C. Building and training a generative AI model from scratch by using specific data that a customer owns.
- D. Refining an existing third-party generative AI foundation model (FM) by fine-tuning the model by using data specific to the business.
Answer: C
NEW QUESTION # 57
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
- A. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
- B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
- C. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
- D. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
Answer: A
Explanation:
I'll continue to format the rest. Let me know if you would like me to provide them all in one go or in parts.
NEW QUESTION # 58
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide.
The employees have minimal experience with technology.
Which solution meets these requirements?
- A. Residual neural network
- B. WaveNet
- C. Support vector machine
- D. Generative pre-trained transformers (GPT)
Answer: D
Explanation:
Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.
* GPT for Natural Language Processing:
* GPT models are designed for understanding and generating human-like text based on natural language input.
* They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.
* Why Option A is Correct:
* Natural Language Understanding: GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.
* User-Friendly: Requires minimal technology experience from employees, as they provide simple text input.
* Why Other Options are Incorrect:
* B. Residual neural network: Typically used in computer vision tasks, not for natural language- to-SQL conversion.
* C. Support vector machine: Used for classification tasks, not for generating structured queries from text.
* D. WaveNet: A deep generative model for audio data, unrelated to text-to-SQL tasks.
NEW QUESTION # 59
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
- A. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
- B. Evaluate the models by using a human workforce and custom prompt datasets.
- C. Use public model leaderboards to identify the model.
- D. Evaluate the models by using built-in prompt datasets.
Answer: B
Explanation:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
* Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
* Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
* Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
* Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner References:
* Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences
NEW QUESTION # 60
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