A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

• A.Create a NAT gateway within the corporate VPC.
• B.Route Amazon SageMaker traffic through an on-premises network.
• C.Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
• D.Create VPC peering with Amazon VPC hosting Amazon SageMaker.

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An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

• A.Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
• B.Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
• C.Augment the images in the dataset. Use open source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
• D.Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.
• E.Use Amazon Rekognition Custom Labels to train a new model.
• F.Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.

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A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

• A.Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
• B.Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
• C.Redeploy the model as a batch transform job on an M5 instance.
• D.Redeploy the model on a P3dn instance.

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While reviewing the histogram for residuals on regression evaluation data a Machine Learning Specialist notices that the residuals do not form a zero-centered bell shape as shown What does this mean?

• A.The model might have prediction errors over a range of target values.
• B.The model is predicting its target values perfectly.
• C.There are too many variables in the model
• D.The dataset cannot be accurately represented using the regression model

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When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A.The output path specifying where on an Amazon S3 bucket the trained model will persist.
• B.The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• C.The validation channel identifying the location of validation data on an Amazon S3 bucket.
• D.Hyperparameters in a JSON array as documented for the algorithm used.
• E.The training channel identifying the location of training data on an Amazon S3 bucket.
• F.The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.

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