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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | USD 100 |
| Related Certifications: | AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate AWS Certified Cloud Practitioner |
| Real Exam Qty: | 80 |
| Passing Score: | 700 / 1000 |
| Exam Format: | Multiple response, Multiple choice |
| Available Languages: | Portuguese (Brazil), Korean, English, Simplified Chinese, Japanese |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 211
A company is training its employees on how to structure prompts for foundation models.
Select the correct prompt engineering technique from the following list for each prompt template. Each prompt engineering technique should be selected onetime. (SelectTHREE.)
* Chain-of-thought reasoning
* Few-shot learning
* Zero-shot learning
Answer:
Explanation:
NEW QUESTION # 212
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 213
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
Answer: B
Explanation:
When a model is trained on a dataset containing confidential or sensitive data, the model may inadvertently learn patterns from this data, which could then be reflected in its inference responses. To ensure that a model does not generate responses based on confidential data, the most effective approach is to remove the confidential data from the training dataset and then retrain the model.
Explanation of Each Option:
Option A (Correct): "Delete the custom model. Remove the confidential data from the training dataset.
Retrain the custom model."This option is correct because it directly addresses the core issue: the model has been trained on confidential data. The only way to ensure that the model does not produce inferences based on this data is to remove the confidential information from the training dataset and then retrain the model from scratch. Simply deleting the model and retraining it ensures that no confidential data is learned or retained by the model. This approach follows the best practices recommended by AWS for handling sensitive data when using machine learning services like Amazon Bedrock.
Option B: "Mask the confidential data in the inference responses by using dynamic data masking."This option is incorrect because dynamic data masking is typically used to mask or obfuscate sensitive data in a database.
It does not address the core problem of the model beingtrained on confidential data. Masking data in inference responses does not prevent the model from using confidential data it learned during training.
Option C: "Encrypt the confidential data in the inference responses by using Amazon SageMaker."This option is incorrect because encrypting the inference responses does not prevent the model from generating outputs based on confidential data. Encryption only secures the data at rest or in transit but does not affect the model's underlying knowledge or training process.
Option D: "Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS)."This option is incorrect as well because encrypting the data within the model does not prevent the model from generating responses based on the confidential data it learned during training. AWS KMS can encrypt data, but it does not modify the learning that the model has already performed.
AWS AI Practitioner References:
Data Handling Best Practices in AWS Machine Learning: AWS advises practitioners to carefully handle training data, especially when it involves sensitive or confidential information. This includes preprocessing steps like data anonymization or removal of sensitive data before using it to train machine learning models.
Amazon Bedrock and Model Training Security: Amazon Bedrock provides foundational models and customization capabilities, but any training involving sensitive data should follow best practices, such as removing or anonymizing confidential data to prevent unintended data leakage.
NEW QUESTION # 214
Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?
Answer: B
Explanation:
AI governance frameworks aim to build trust and deploy human-centered AI technologies by establishing guidelines and policies for data usage, transparency, responsible AI practices, and compliance with regulations. This ensures ethical and accountable AI development and deployment.
Exact Extract from AWS AI Documents:
From the AWS Documentation on Responsible AI:
"AI governance frameworks establish trust in AI technologies by developing policies and guidelines for data management, transparency, responsible AI practices, and compliance with regulatory requirements, ensuring human-centered and ethical AI deployment." (Source: AWS Documentation, Responsible AI Governance) Detailed Option A: Expanding initiatives across business units to create long-term business valueWhile expanding initiatives can drive value, it is not a core characteristic of AI governance frameworks focused on trust and human-centered AI.
Option B: Ensuring alignment with business standards, revenue goals, and stakeholder expectationsAlignment with business goals is important but not specific to AI governance frameworks for building trust and ethical AI deployment.
Option C: Overcoming challenges to drive business transformation and growthOvercoming challenges is a general business goal, not a defining characteristic of AI governance frameworks.
Option D: Developing policies and guidelines for data, transparency, responsible AI, and complianceThis is the correct answer. This option directly describes the core components of AI governance frameworks that ensure trust and ethical AI deployment.
Reference:
AWS Documentation: Responsible AI Governance (https://aws.amazon.com/machine-learning/responsible-ai/) AWS AI Practitioner Learning Path: Module on AI Governance AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
NEW QUESTION # 215
A company is deploying an AI-powered loan approval system. The company must comply with data governance regulations for AI.
Which solution will meet these requirements?
Answer: A
Explanation:
Implementing data lifecycle management that tracks and manages AI training data is the only option that directly addresses governance, traceability, compliance, and audit requirements throughout an AI/ML lifecycle.
AWS treats governance as a responsible AI dimension involving the incorporation of appropriate practices throughout the AI supply chain, including providers and deployers. AWS also emphasizes appropriate collection, use, management, security, and traceability of data.
A practical AWS example is Amazon SageMaker AI Lineage Tracking. AWS states that SageMaker ML Lineage Tracking "creates and stores information about the steps of a machine learning (ML) workflow from data preparation to model deployment." It enables organizations to track model and dataset lineage and establish governance and audit standards.
For a regulated loan-approval system, an organization should be capable of determining what training data was used, where it originated, how it was processed, which training job consumed it, which model artifact was generated, and how the resulting model progressed toward deployment. AWS lineage entities can associate training datasets with training jobs and subsequent model artifacts, supporting compliance verification and reproducibility.
Option A undermines governance because changing outputs without auditable records reduces accountability and traceability.
Option C is incorrect because performance optimization cannot supersede mandatory regulatory requirements such as approved data residency, retention, and processing controls.
Option D is also incorrect. Synthetic data can sometimes reduce exposure of real personal information, but using synthetic data does not automatically satisfy AI governance requirements. Organizations still need lifecycle controls, documentation, provenance, validation, security, auditing, and regulatory compliance.
Data governance should cover acquisition, storage, processing, access, lineage, retention, deletion, and permitted uses of data throughout the ML lifecycle.
NEW QUESTION # 216
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