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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Machine Learning – Specialty |
| Exam Duration: | 90 minutes |
| Passing Score: | 700 (scaled score 100–1000) |
| Exam Price: | 100 USD |
| Exam Format: | Ordering, Multiple choice, Multiple response |
| Available Languages: | Japanese, English, Korean, Simplified Chinese, Traditional Chinese |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Path AWS Certified AI Practitioner Official Training |
| Exam Registration: | Pearson VUE Scheduling AWS Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored or testing center delivery |
| Pre Condition: | No required prerequisites; recommended basic understanding of cloud computing and general IT concepts |
| Official Syllabus URL: | https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.html |
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NEW QUESTION # 40
A company wants to classify images of different objects based on custom features extracted from a dataset.
Which solution will meet this requirement with the LEAST development effort?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Fine-tuning a pre-trained deep learning model (transfer learning) provides the lowest development effort because:
* The model already understands general visual features
* Only minimal training is required on the custom dataset
* Feature extraction is handled automatically
AWS guidance strongly recommends transfer learning for image classification tasks when development speed and efficiency are priorities.
Why the other options are incorrect:
* A and D require extensive feature engineering and tuning.
* C (GANs) are designed for data generation, not classification.
AWS AI document references:
* Transfer Learning for Computer Vision on AWS
* Image Classification Best Practices with SageMaker
* Deep Learning Model Reuse and Fine-Tuning
NEW QUESTION # 41
A company is using a large collection of web data to produce a large language model (LLM). The company completes a random initialization of the model's weights. Next, the company fits the model to the data through a language-modeling objective function.
Which stage of the model training process does this scenario describe?
Answer: A
Explanation:
The verified answer is B. Pre-training . The scenario describes the first major training stage for a large language model, where the model starts with randomly initialized weights and learns broad language patterns from a very large corpus of unlabeled or self-supervised text data. AWS describes large language models as very large deep learning models that are pre-trained on vast amounts of data . In this stage, the model is not yet being customized for a narrow business task; instead, it learns general-purpose linguistic and semantic representations from large-scale text.
AWS machine learning guidance explains that pre-training teaches the model broad linguistic and semantic patterns , including grammar, context, world knowledge, reasoning, and token prediction, using self- supervised learning approaches such as masked language modeling or causal language modeling. That exactly matches the question's description: random weight initialization followed by fitting the model to a large web dataset using a language-modeling objective. This is the core training process used to create a foundation model or LLM before later adaptation.
Option A. Fine-tuning is incorrect because AWS defines fine-tuning as training a pretrained model on a new dataset rather than training from scratch. Fine-tuning adapts an existing model for a specific task or domain and usually requires less data and less training time than pre-training.
Option C. Model selection is incorrect because model selection refers to choosing an appropriate model or architecture, not training the model weights from random initialization. Option D. Deployment is also incorrect because AWS describes deployment as the stage where a trained model is made available for inference or predictions, such as through SageMaker inference endpoints.
Therefore, because the company is training an LLM from initial weights on a large web corpus using a language-modeling objective, the stage is pre-training .
NEW QUESTION # 42
An AI practitioner performed continued pre-training on a foundation model (FM). After model deployment, the AI practitioner discovered that the model was exposing sensitive company information that was inadvertently included in the training data.
Which security risk does this scenario represent?
Answer: A
Explanation:
The verified answer is B. Data leakage. The model exposes sensitive company information because that information was inadvertently included in training data. AWS guidance warns that using sensitive data to train models can cause unintended disclosure of data from model output, including data leakage during inference. This is exactly the scenario: sensitive information entered the training process and later appeared in model output after deployment.
AWS security reference guidance for generative AI model customization identifies sensitive information disclosure as a threat where a model trained on datasets containing PII leaks sensitive information during inference. Although the question says company information rather than PII specifically, the same security principle applies: protected data included in training or continued pre-training can be memorized or reproduced by the model later.
Jailbreaking is incorrect because jailbreaking refers to attempts to bypass model safety controls through adversarial prompts. The problem here is not that a user bypassed safety controls; the sensitive information was already embedded through training data.
Contextual grounding is incorrect because grounding is a technique for improving answer accuracy by connecting model responses to trusted context, such as RAG sources. It is not a security risk.
Prompt injection is incorrect because prompt injection involves malicious input that attempts to override or manipulate model behavior at inference time. The question does not describe a malicious prompt. It describes accidental exposure from training data.
Therefore, the correct security risk is data leakage.
NEW QUESTION # 43
A company uses Amazon Comprehend to analyze customer feedback. A customer has several unique trained models. The company uses Comprehend to assign each model an endpoint. The company wants to automate a report on each endpoint that is not used for more than 15 days.
Answer: C
Explanation:
The correct answer is B because Amazon CloudWatch provides monitoring capabilities that include tracking usage metrics for Amazon Comprehend endpoints, such as invocation counts. You can configure CloudWatch to collect these metrics and create custom dashboards or alarms to report when an endpoint has zero usage over a period (e.g., 15 days).
From AWS documentation:
"Amazon CloudWatch enables you to collect and track metrics for Comprehend endpoints, create alarms, and automatically react to changes in your AWS resources." This allows automated reporting and alerting for underused or idle endpoints.
Explanation of other options:
A). AWS Trusted Advisor focuses on general AWS resource optimization, security, and limits but does not track endpoint usage.
C). AWS CloudTrail tracks API calls but does not provide time-based monitoring or usage analysis over time.
D). AWS Config is used to track configuration changes and compliance, not endpoint usage metrics.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon CloudWatch Metrics for Amazon Comprehend
* AWS Certified Machine Learning Specialty Guide - Monitoring and Logging Section
* AWS Cloud Operations Guide - Resource Utilization Monitoring
NEW QUESTION # 44
A financial company has offices in different countries worldwide. The company requires that all API calls between generative AI applications and foundation models (FM) must not travel across the public internet.
Which AWS service should the company use?
Answer: C
Explanation:
AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, ensuring traffic does not traverse the public internet.
A is correct:
"AWS PrivateLink provides private connectivity to services across VPCs, keeping API traffic off the public internet." (Reference: AWS PrivateLink Overview) B (Amazon Q) is a generative AI assistant, not a network security/control tool.
C (CloudFront) is a CDN, not for private API calls.
D (CloudTrail) is for logging and monitoring, not secure connectivity.
NEW QUESTION # 45
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