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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner (AIF-C01) |
| Exam Number: | AIF-C01 |
| Available Languages: | Portuguese (Brazil), Japanese, English, Korean, Spanish, Simplified Chinese |
| Exam Format: | Multiple choice, Multiple response |
| Real Exam Qty: | 65 |
| Exam Price: | $100 USD |
| Passing Score: | 700/1000 |
| Certificate Validity Period: | 3 years |
| Related Certifications: | AWS Certified Cloud Practitioner |
| Exam Duration: | 90 minutes |
| Recommended Training: | AWS Skill Builder - AI Practitioner Learning Plan AWS Certified AI Practitioner Official Exam Guide |
| Exam Registration: | AWS Certification Portal AWS Training and Certification Registration |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam or in-person test center |
| Pre Condition: | No formal prerequisites required. Recommended: basic understanding of cloud computing and general AI/ML concepts. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 75
Which phase of the ML lifecycle determines compliance and regulatory requirements?
Answer: A
Explanation:
The business goal identification phase of the ML lifecycle involves defining the objectives of the project and understanding the requirements, including compliance and regulatory considerations. This phase ensures the ML solution aligns with legal and organizational standards before proceeding to technical stages like data collection or model training.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The business goal identification phase involves defining the problem to be solved, identifying success metrics, and determining compliance and regulatory requirements to ensure the ML solution adheres to legal and organizational standards." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Option A: Feature engineeringFeature engineering involves creating or selecting features for model training, which occurs after compliance requirements are identified. It does not address regulatory concerns.
Option B: Model trainingModel training focuses on building the ML model using data, not on determining compliance or regulatory requirements.
Option C: Data collectionData collection involves gathering data for training, but compliance and regulatory requirements (e.g., data privacy laws) are defined earlier in the business goal identification phase.
Option D: Business goal identificationThis is the correct answer. This phase ensures that compliance and regulatory requirements are considered at the outset, shaping the entire ML project.
Reference:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: ML Workflow (https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-mlconcepts.html) AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/)
NEW QUESTION # 76
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A . Include more diverse training data. Fine-tune the model again by using the new data:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation)
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
NEW QUESTION # 77
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?
Answer: A
NEW QUESTION # 78
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?
Answer: C
Explanation:
Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model's parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks." (Source: AWS Bedrock User Guide, Model Customization) Detailed Explanation:
* Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.
* Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.
* Option C: Model quantizationModel quantization reduces the precision of a model's weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.
* Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.
References:
AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide
/custom-models.html)
AWS AI Practitioner Learning Path: Module on Model Training and Customization Amazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest
/dg/algos.html)
NEW QUESTION # 79
A software company wants to use a large language model (LLM) for workflow automation. The application will transform user messages into JSON files. The company will use the JSON files as inputs for data pipelines.
The company has a labeled dataset that contains user messages and output JSON files.
Which solution will train the LLM for workflow automation?
Answer: B
Explanation:
Fine-tuning is the process of training a pre-trained LLM with a labeled dataset specific to a desired task-in this case, mapping user messages to JSON outputs. Fine-tuning leverages supervised learning to specialize the model's outputs.
C is correct:
"Fine-tuning is a supervised learning approach in which a model is further trained on a custom, labeled dataset to adapt to a specific use case." (Reference: Amazon Bedrock Fine-Tuning, AWS Certified AI Practitioner Study Guide) A is incorrect-unsupervised learning does not use labeled data.
B (continued pre-training) uses unlabeled data.
D (RLHF) uses reward signals and human feedback, not direct labeled input/output pairs.
NEW QUESTION # 80
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