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NEW QUESTION # 400
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?
Answer: D
Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.
NEW QUESTION # 401
A company is creating a model to label credit card transactions. The company has a large volume of sample transaction data to train the model. Most of the transaction data is unlabeled. The data does not contain confidential information. The company needs to obtain labeled sample data to fine-tune the model.
Answer: E
Explanation:
* Amazon SageMaker Ground Truth lets you create data labeling jobs and can integrate with Amazon Mechanical Turk (a distributed human workforce) to label large unlabeled datasets.
* A (batch inference) applies models to already-trained data, not labeling.
* B (PyTorch Distributed) is for distributed training, not labeling.
* D (OCR) applies only to text extraction from images, not transactions.
* E is incorrect; Ground Truth is the service for labeling, not "AI labeling job."
# Reference:
AWS Documentation - SageMaker Ground Truth
NEW QUESTION # 402
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?
Answer: A
NEW QUESTION # 403
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. 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,B
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 References:
* 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 # 404
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?
Answer: D
Explanation:
Object detection is the correct strategy for automatically identifying and categorizing animals in photos.
* Object Detection:
* A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.
* Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.
* Why Option A is Correct:
* Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.
* Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.
* Why Other Options are Incorrect:
* B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.
* C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.
* D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.
NEW QUESTION # 405
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