A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?

• A.Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.
• B.Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
• C.Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers.
  Implement cross-entropy loss functions.
• D.Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.

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An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

• A.Use Amazon Rekognition to automatically label the dataset.
• B.Train the deep learning model directly on the raw data. Let the model infer the labels by itself.
• C.Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements.
• D.Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.
• E.Use Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.

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A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

• A.Transfer the data to a new S3 bucket that provides S3 Express One Zone storage. Adjust the training job to use the new S3 bucket.
• B.Create an Amazon FSx for Lustre file system. Link the file system to the existing S3 bucket. Adjust the training job to read from the file system.
• C.Create an Amazon Elastic File System (Amazon EFS) file system. Transfer the existing data to the file system. Adjust the training job to read from the file system.
• D.Create an Amazon ElastiCache (Redis OSS) cluster. Link the Redis OSS cluster to the existing S3 bucket. Stream the data from the Redis OSS cluster directly to the training job.

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A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks.
The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Export the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
• B.Import the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
• C.Use code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
• D.Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.

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A machine learning engineer is preparing a data frame for a supervised learning task with the Amazon SageMaker Linear Learner algorithm. The ML engineer notices the target label classes are highly imbalanced and multiple feature columns contain missing values. The proportion of missing values across the entire data frame is less than 5%.
What should the ML engineer do to minimize bias due to missing values?

• A.Replace each missing value by the mean or median across non-missing values in same row.
• B.Delete observations that contain missing values because these represent less than 5% of the data.
• C.Replace each missing value by the mean or median across non-missing values in the same column.
• D.For each feature, approximate the missing values using supervised learning based on other features.

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