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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Related Certifications: | AWS Certified Machine Learning – Specialty AWS Certified Cloud Practitioner |
| Exam Price: | 100 USD |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Available Languages: | Korean, Traditional Chinese, English, Simplified Chinese, Japanese |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple response, Ordering, Multiple choice |
| Passing Score: | 700 (scaled score 100–1000) |
| Exam Duration: | 90 minutes |
| Recommended Training: | AWS Certified AI Practitioner Official Training AWS Skill Builder - AI Practitioner Learning Path |
| 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 # 52
A company is building a job recommendation system based on job posting data and job seeker user profiles.
The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.
Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Fairness is a core principle of AWS Responsible AI. It requires that AI systems:
* Do not produce biased or discriminatory outcomes
* Treat individuals and groups equitably
* Are evaluated and mitigated for bias across protected attributes
AWS Responsible AI guidance specifically highlights fairness as the principle used to identify, measure, and mitigate bias in AI systems such as recommendation engines.
Why the other options are incorrect:
* Governance (A) focuses on oversight and policies.
* Explainability (B) explains decisions but does not directly mitigate bias.
* Controllability (C) focuses on human oversight and intervention.
AWS AI document references:
* AWS Responsible AI Principles
* Bias and Fairness in ML Systems
* Responsible AI Best Practices on AWS
NEW QUESTION # 53
A mining company is building a land classification system by using a dataset of several million images. The dataset contains raw image files in various formats. The images do not contain annotations or metadata that indicates types of land cover.
Which type of dataset has the company collected for modeling?
Answer: C
Explanation:
The image collection is an unstructured, unlabeled dataset.
First, image files are normally classified as unstructured data because their contents are not represented according to a fixed tabular schema consisting of defined rows and columns. AWS data architecture guidance explicitly identifies images as examples of unstructured data. AWS describes unstructured data as information that does not conform to a predefined data model and is typically stored as individual files.
Second, the data is unlabeled because none of the images contains an annotation or target specifying the land-cover class represented by that image. The scenario states that there is no metadata indicating whether an image represents forest, water, agricultural land, urban land, desert terrain, or another classification.
AWS guidance on machine-learning datasets distinguishes supervised labeled datasets from unlabeled inputs. For example, SageMaker Canvas requires image labels when training a single-label image prediction model and instructs users to assign labels to images that are currently unlabeled.
A structured, labeled dataset, option A, would typically contain defined attributes or fields together with known target labels.
A semi-structured dataset, option C, might include data such as JSON or XML that does not use a strict relational schema but contains explicit structural elements or identifiers. Raw images do not fit that definition merely because they use different image file formats.
Option D correctly says unlabeled but incorrectly describes the data as structured.
For supervised land classification, the mining company would typically need to create ground-truth annotations that associate images or image regions with their correct land-cover classes. AWS services such as SageMaker Ground Truth can support data-labeling workflows when supervised training data must be produced at scale.
NEW QUESTION # 54
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
Answer: B
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.
NEW QUESTION # 55
A company deploys a custom ML model on Amazon SageMaker AI. The company uses the model to build a generative AI application for a healthcare recommendation system.
The company tests the application and finds a potential bias issue. The application consistently recommends different treatment approaches for patients who have identical medical conditions based on patient demographic information.
The company needs a solution to ensure that the application does not generate biased recommendations.
Which solution will meet this requirement?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI best practices emphasize that bias should be detected, measured, mitigated, and monitored throughout the ML lifecycle, especially for sensitive domains such as healthcare. When biased outcomes are observed, AWS guidance recommends addressing bias at the data and model level, not only at the output level.
Using Amazon SageMaker Clarify aligns directly with AWS Responsible AI principles because it is designed to:
Detect and quantify bias in datasets and model predictions across sensitive attributes such as demographic groups Provide pre-training and post-training bias metrics, allowing practitioners to identify where bias originates Support data-centric mitigation, including improving dataset balance and representativeness After identifying bias with SageMaker Clarify, collecting additional balanced training data and retraining the model helps ensure that:
The model learns from a more representative dataset
Disparate treatment recommendations based on demographics are reduced
Fairness is improved while maintaining clinical accuracy
Why the other options are not sufficient or aligned with AWS best practices:
B . Prompt engineering can influence outputs but does not address underlying data or model bias and is not sufficient for regulated, high-risk domains like healthcare.
C . Content filtering removes outputs after generation but does not prevent biased decision-making by the model itself.
D . Separate FM endpoints by demographic group increases the risk of reinforcing bias and violates fairness principles rather than mitigating them.
AWS AI Study Guide Reference:
AWS Responsible AI principles: Fairness and Governance
Amazon SageMaker Clarify: bias detection and mitigation
AWS best practices for ML in high-risk domains such as healthcare
NEW QUESTION # 56
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.
AWS generative AI guidance explains that:
* Prompts can define tone, voice, and response structure
* Iterative refinement ensures consistent outputs
* Prompt refinement requires no model retraining
Why the other options are incorrect:
* Token limits (A) affect length, not tone.
* Batch inferencing (B) affects processing mode, not response style.
* Higher temperature (D) increases randomness, reducing consistency.
AWS AI document references:
* Prompt Engineering Best Practices
* Controlling Model Output Tone
* Using Prompts with Foundation Models on AWS
NEW QUESTION # 57
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