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NEW QUESTION 22
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?

A. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.C. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.

Answer: A

 

NEW QUESTION 23
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. Hyperparameters in a JSON array as documented for the algorithm used.B. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.C. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.D. The output path specifying where on an Amazon S3 bucket the trained model will persist.E. The validation channel identifying the location of validation data on an Amazon S3 bucket.F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: B,D,F

 

NEW QUESTION 24
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

A. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizersB. Amazon Comprehend syntax analysts and entity detectionC. Natural Language Toolkit (NLTK) stemming and stop word removalD. Amazon SageMaker BlazingText allow mode

Answer: A

 

NEW QUESTION 25
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

A. Create a NAT gafjway within the corporate VPC.B. Create VPC peering with Amazon VPC hosting Amazon SageMaker.C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.D. Route Amazon SageMaker traffic through an on-premises network.

Answer: C

 

NEW QUESTION 26
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

A. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.C. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.D. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: B

Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.

 

NEW QUESTION 27
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