A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?

• A.Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
• B.Write a direct connection to the SQL database within the notebook and pull data in
• C.Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
• D.Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.

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You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

• A.Change the learning rate
• B.Change the optimizer
• C.Reduce the image shape
• D.Reduce the batch size

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You are building a linear model with over 100 input features, all with values between -1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

• A.Use Principal Component Analysis to eliminate the least informative features.
• B.Use L1 regularization to reduce the coefficients of uninformative features to 0.
• C.After building your model, use Shapley values to determine which features are the most informative.
• D.Use an iterative dropout technique to identify which features do not degrade the model when removed.

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You work for the AI team of an automobile company, and you are developing a visual defect detection model using TensorFlow and Keras. To improve your model performance, you want to incorporate some image augmentation functions such as translation, cropping, and contrast tweaking. You randomly apply these functions to each training batch. You want to optimize your data processing pipeline for run time and compute resources utilization. What should you do?

• A.Embed the augmentation functions dynamically as part of Keras generators.
• B.Use Dataflow to create all possible augmentations, and store them as TFRecords.
• C.Use Dataflow to create the augmentations dynamically per training run, and stage them as TFRecords.
• D.Embed the augmentation functions dynamically in the tf.Data pipeline.

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A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available.
Which model is MOST likely to provide the best results in Amazon SageMaker?

• A.Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
• B.Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
• C.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
• D.Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.

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