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NEW QUESTION 25 A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario?

• A. Store datasets as global tables in Amazon DynamoDB.
• B. Store datasets as files in Amazon S3.
• C. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
• D. Store datasets as tables in a multi-node Amazon Redshift cluster.

Answer: B   NEW QUESTION 26 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. Include a copy of the samples in the test dataset in the training dataset.
• B. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
• C. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
• D. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
• E. Add more deep trees to the random forest to enable the model to learn more features.

Answer: A,C   NEW QUESTION 27 A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations. What should the ML specialist do to resolve the violations?

• A. Retrain the model again by using a combination of the original training set and the new training set.
• B. Delete the endpoint and recreate it with the original configuration.
• C. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
• D. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.

Answer: D   NEW QUESTION 28 A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible. What approach should the Specialist take to accomplish these tasks?

• A. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.
• B. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
• C. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
• D. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

Answer: A   NEW QUESTION 29 ......