A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

• A.Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
• B.Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
• C.Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
• D.Use AWS Glue to catalogue the data and Amazon Athena to run queries.

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A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions - Here is an example from the dataset
"The quck BROWN FOX jumps over the lazy dog "
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Select THREE)

• A.Normalize all words by making the sentence lowercase
• B.One-hot encode all words in the sentence
• C.Correct the typography on "quck" to "quick."
• D.Tokenize the sentence into words.
• E.Perform part-of-speech tagging and keep the action verb and the nouns only
• F.Remove stop words using an English stopword dictionary.

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A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

• A.Change the cost function so that false negatives have a higher impact on the cost value than false positives.
• B.Change the cost function so that false positives have a higher impact on the cost value than false negatives.
• C.Include a copy of the samples in the test dataset in the training dataset.
• D.Add more deep trees to the random forest to enable the model to learn more features.
• E.Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

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An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models.
During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.
Which of the following should be used to resolve this issue? (Choose two.)

• A.Add vanishing gradient to the model.
• B.Perform data augmentation on the training data.
• C.Make the neural network architecture complex.
• D.Use gradient checking in the model.
• E.Add L2 regularization to the model.

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A large consumer goods manufacturer has the following products on sale
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3 Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products The company wants to predict the demand for a new product that will soon be launched Which solution should a Machine Learning Specialist apply?

• A.Train a custom ARIMA model to forecast demand for the new product.
• B.Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product
• C.Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
• D.Train a custom XGBoost model to forecast demand for the new product

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