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NEW QUESTION 39
A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.
How should the data scientist accomplish this?

• A. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
• B. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.
• C. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.
• D. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.

Answer: A

NEW QUESTION 40
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

• A. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_tyce of classifier
• B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
• C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor
• D. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data

Answer: B

NEW QUESTION 41
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

• A. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
• B. Use Amazon ML for both feature engineering and model development.
• C. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
• D. Use an Amazon SageMaker notebook for both feature engineering and model development

Answer: C

NEW QUESTION 42
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

• A. Perform one-hot encoding on highly correlated features
• B. Apply the Pearson correlation coefficient
• C. Use matrix multiplication on highly correlated features.
• D. Create a new feature space using principal component analysis (PCA)

Answer: D

NEW QUESTION 43
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