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| Certification Vendor: | Amazon AWS |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Exam Price: | 100 USD |
| Exam Duration: | 90 minutes |
| Certificate Validity Period: | 3 years |
| Exam Format: | Multiple choice, Ordering, Multiple response |
| Available Languages: | Japanese, Traditional Chinese, English, Simplified Chinese, Korean |
| Real Exam Qty: | 65 (50 scored, 15 unscored) |
| Related Certifications: | AWS Certified Machine Learning โ Specialty AWS Certified Cloud Practitioner |
| Passing Score: | 700 (scaled score 100โ1000) |
| 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 # 134
A financial company built an AI assistant on Amazon Bedrock for customer service teams. The company needs to automatically enforce compliance guidelines on every AI-generated response before delivery to ensure adherence to financial regulations.
Which Amazon Bedrock resource meets these requirements?
Answer: A
Explanation:
Amazon Bedrock Guardrails Automated Reasoning checks use formal logic to validate statements generated by foundation models against predefined policies.
Organizations can convert business rules and compliance requirements into automated reasoning policies. The checks evaluate whether generated statements are consistent with those rules and return findings that identify contradictions or unsupported conclusions.
These capabilities are particularly valuable in regulated industries, including financial services, where incorrect guidance may create compliance risks.
Knowledge Bases document chunking supports information retrieval. Judge-model scoring evaluates response quality, while Flow aliases manage workflow versions.
An important AWS implementation distinction is that Automated Reasoning checks operate in detection mode. They return validation findings rather than automatically blocking responses.
Consequently, the application must inspect those findings and prevent noncompliant responses from being delivered.
Option C is therefore the intended answer.
NEW QUESTION # 135
A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues.
Which AWS service or feature meets these requirements?
Answer: C
Explanation:
Amazon SageMaker Model Monitor is specifically designed to automatically detect and alert on changes in model quality, such as data drift, prediction drift, or other anomalies in model performance once deployed.
D is correct:
"Amazon SageMaker Model Monitor continuously monitors the quality of machine learning models in production. It automatically detects concept drift, data drift, and other quality issues, enabling teams to take corrective actions." (Reference: Amazon SageMaker Model Monitor Documentation, AWS Certified AI Practitioner Study Guide) A (JumpStart) provides prebuilt solutions and models, not monitoring.
B (HyperPod) is for large-scale training, not model monitoring.
C (Data Wrangler) is for data preparation, not ongoing model quality monitoring.
NEW QUESTION # 136
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?
Answer: C
NEW QUESTION # 137
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?
Answer: D
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 # 138
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)
Answer: A,C
Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner Reference:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.
NEW QUESTION # 139
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