An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

• A.Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
• B.Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
• C.Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF- IDF) scores to identify and extract relevant keywords.
• D.Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre- processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.

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A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2.
Which solution will meet these requirements?

• A.Create a second production variant for v2. Assign 1% of the traffic to v2 and 99% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
• B.Create a second production variant for v2. Assign 10% of the traffic to v2 and 90% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
• C.Deploy v2 to a new endpoint. Turn on data capturing for the production endpoint. Write a script to pass 100% of input data to v2. If v2 performs as expected, deactivate the v1 endpoint and direct the traffic to v2.
• D.Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production.

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Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

• A.Use AWS Glue DataBrew built-in features to oversample the minority class.
• B.Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
• C.Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
• D.Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

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A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

• A.Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
• B.Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded.
• C.Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule.
• D.Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.

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A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

• A.Use random forests to classify sentiments of the chat conversations.
• B.Use Amazon Rekognition to analyze sentiments of the chat conversations.
• C.Use Amazon Comprehend to analyze sentiments of the chat conversations.
• D.Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

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