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NEW QUESTION 44
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE )
- A. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
- B. An 1AM role that allows access to the specific S3 bucket
- C. An S3 bucket owner that matches the notebook owner
- D. An AWS KMS key policy that allows access to the customer master key (CMK)
- E. A SageMaker notebook security group that allows access to Amazon S3
- F. A permissive S3 bucket policy
Answer: A,B,D
NEW QUESTION 45
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A. Recall
- B. Area Under the ROC Curve (AUC)
- C. Misclassification rate
- D. Mean absolute percentage error (MAPE)
Answer: B
Explanation:
Another benefit of using AUC is that it is classification-threshold-invariant like log loss.
https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know- aa97784ff226
NEW QUESTION 46
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?
- A. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
- B. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
- C. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
- D. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
Answer: B
Explanation:
https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html
NEW QUESTION 47
A company will use Amazon SageMaker to train and host a machine learning (ML) model for a marketing campaign. The majority of data is sensitive customer dat a. The data must be encrypted at rest. The company wants AWS to maintain the root of trust for the master keys and wants encryption key usage to be logged.
Which implementation will meet these requirements?
- A. Use encryption keys that are stored in AWS Cloud HSM to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
- B. Use AWS Security Token Service (AWS STS) to create temporary tokens to encrypt the ML storage volumes, and to encrypt the model artifacts and data in Amazon S3.
- C. Use customer managed keys in AWS Key Management Service (AWS KMS) to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.
- D. Use SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.
Answer: C
NEW QUESTION 48
A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format. During model evaluation, the data scientist discovered that the model recommends certain stopwords such as “a,” “an,” and “the” as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?
- A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
- B. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
- C. Remove the stopwords from the blog post data by using the Count Vectorizer function in the scikit-learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
- D. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
Answer: C
NEW QUESTION 49
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