Newest AIF-C01 Exam Questions and AWS Certified AI Practitioner Learning Reference Files

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 2	Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 3	Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.
Topic 4	Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 5	Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.

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Quiz 2025 AIF-C01: High-quality Valid AWS Certified AI Practitioner Test Labs

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Amazon AWS Certified AI Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?

A. Amazon Lex
B. AWS Glue
C. Amazon Kinesis Data Streams
D. Amazon Rekognition

Answer: B

Explanation:
AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.
* AWS Glue:
* A fully managed extract, transform, and load (ETL) service that makes it easy to prepare and transform unstructured data into a structured format.
* Provides a range of tools for cleaning, enriching, and cataloging data, making it ready for data scientists to use in ML models.
* Why Option D is Correct:
* Data Transformation: AWS Glue can handle large volumes of data and transform unstructured data into structured formats efficiently.
* Integrated ML Support: Glue integrates with other AWS services to support ML workflows.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building chatbots, not for data transformation.
* B. Amazon Rekognition: Used for image and video analysis, not for data transformation.
* C. Amazon Kinesis Data Streams: Handles real-time data streaming, not suitable for batch transformation of large volumes of unstructured data.

NEW QUESTION # 66
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?

A. WaveNet
B. XGBoost
C. Generative adversarial network (GAN)
D. Residual neural network

Answer: C

NEW QUESTION # 67
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns.
The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

A. Create effective prompts that provide clear instructions and context to guide the model's generation.
B. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
C. Select a large, diverse dataset to pre-train a new generative model.
D. Increase the model's complexity by adding more layers to the model's architecture.

Answer: A

Explanation:
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.
* Effective Prompt Engineering:
* Involves crafting prompts that clearly outline the desired tone, style, and content guidelines for the model.
* By providing explicit instructions in the prompts, the company can guide the AI to generate content that matches the brand's voice and messaging.
* Why Option C is Correct:
* Guides Model Output: Ensures the generated content adheres to specific brand guidelines by shaping the model's response through the prompt.
* Flexible and Cost-effective: Does not require retraining or modifying the model, which is more resource-efficient.
* Why Other Options are Incorrect:
* A. Optimize the model's architecture and hyperparameters: Improves model performance but does not specifically address alignment with brand voice.
* B. Increase model complexity: Adding more layers may not directly help with content alignment.
* D. Pre-training a new model: Is a costly and time-consuming process that is unnecessary if the goal is content alignment.

NEW QUESTION # 68
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?

A. Customize the model by using fine-tuning.
B. Use Provisioned Throughput.
C. Increase the number of tokens in the prompt.
D. Decrease the number of tokens in the prompt.

Answer: D

Explanation:
Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
* Token Reduction Strategy:
* By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.
* Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.
* Why Option B is Correct:
* Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.
* Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.
* Why Other Options are Incorrect:
* A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.
* C. Increase the number of tokens: Would increase costs, not lower them.
* D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

NEW QUESTION # 69
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

A. Exploratory data analysis
B. Data pre-processing
C. Hyperparameter tuning
D. Feature engineering

Answer: A

Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.

NEW QUESTION # 70
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NEWEST AIF-C01 EXAM QUESTIONS AND AWS CERTIFIED AI PRACTITIONER LEARNING REFERENCE FILES

Newest AIF-C01 Exam Questions and AWS Certified AI Practitioner Learning Reference Files