Which option is a benefit of ongoing pre-training when fine-tuning a foundation model (FM)?
Answer(s): B
Ongoing pre-training helps enhance a foundation model's performance by continuously updating it with new data, thereby improving its ability to generalize and perform well on different tasks. The other options do not directly relate to the benefits of ongoing pre-training.
A company has developed a generative AI model for customer segmentation. The model has been deployed in the company's production environment for a long time. The company recently noticed some inconsistency in the model's responses. The company wants to evaluate model bias and drift.Which AWS service or feature meets these requirements?
Answer(s): A
SageMaker Model Monitor continuously collects inference and ground-truth data to automatically detect data drift, concept drift, and bias shifts in production models. By configuring baseline metrics and thresholds, the company can track inconsistencies in responses over time and receive alerts whenever bias or drift exceed acceptable limits.
A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM).The company does not want to develop labeling applications or manage a labeling workforce.Which AWS service or feature meets these requirements?
Amazon SageMaker Ground Truth Plus provides managed data labeling services, allowing you to label datasets using human feedback without developing labeling applications or managing a labeling workforce.
A retail company has deployed an ML model to predict if customers will purchase a product. The dataset is highly imbalanced. Only 5% of customers make purchases. The model shows 95% accuracy. However, the company reports that the model rarely identifies actual buyers.Which metric should the company use instead to evaluate the model's performance?
The F1 score is better suited to highly imbalanced classification problems because it balances precision and recall. In this case, overall accuracy is misleading because a model could predict “no purchase” for nearly everyone and still achieve about 95% accuracy while failing to identify actual buyers.
A company stores millions of PDF documents in an Amazon S3 bucket. The company needs to extract the text from the PDFs, generate summaries of the text, and index the summaries for fast searching.Which combination of AWS services will meet these requirements? (Chose two.)
Answer(s): B,E
Amazon Textract can extract text and structured content from PDF documents stored in Amazon S3. AmazonBedrock can then use foundation models to summarize the extracted text and support downstream search workflows by generating concise, semantically useful content for indexing.
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