Online Access Free AWS-Certified-Machine-Learning-Specialty Exam Questions

A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?

• A.Decision tree
• B.Single Perceptron with sigmoidal activation function
• C.Naive Bayesian classifier
• D.Linear support vector machine (SVM)

A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

• A.Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatiblewith Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket usingPipe input mode.
• B.Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Initiate a SageMaker training job using thefull dataset from the S3 bucket using Pipe input mode.
• C.Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the trainingcode is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with anAWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
• D.Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to theinstance. Train on a small amount of the data to verify the training code and hyperparameters. Go back toAmazon SageMaker and train using the full dataset

The displayed graph is from a foresting model for testing a time series.
Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

• A.The model predicts the seasonality well, but not the trend.
• B.The model does not predict the trend or the seasonality well.
• C.The model predicts the trend well, but not the seasonality.
• D.The model predicts both the trend and the seasonality well.

A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

• A.Reduce image resolution and use reduced resolution pixel values as features
• B.Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
• C.Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
• D.Average colors by channel to obtain three-dimensional representations of images.

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