A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

• A.Train a custom classifier by using Amazon Comprehend.
• B.Use a built-in seq2seq model in Amazon SageMaker.
• C.Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.
• D.Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.

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A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

• A.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
• B.Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
• C.Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
• D.Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.

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A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

• A.The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
• B.The dropout rate at the flatten layer should be increased because the model is not generalized enough.
• C.The learning rate should be increased because the optimization process was trapped at a local minimum.
• D.The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

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A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

• A.Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
• B.Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
• C.Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
• D.Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

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

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

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