A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

• A.Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
• B.Add more data to the training set and retrain the model using transfer learning to reduce the bias.
• C.Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
• D.Train a new model using the current neural network architecture.

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A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

• A.Specificity
• B.False positive rate
• C.Accuracy
• D.Area under the precision-recall curve
• E.True positive rate

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

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An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

• A.Listwise deletion
• B.Last observation carried forward
• C.Multiple imputation
• D.Mean substitution

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

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