AWS-Certified-Machine-Learning-Specialty Free Practice Test

The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

1. A. Latent Dirichlet Allocation (LDA)
2. B. Recurrent neural network (RNN)
3. C. K-means
4. D. Convolutional neural network (CNN)

Correct Answer: D

An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers’ current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.
Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

1. A. The XGBoost model provides a true/false binary outpu
2. B. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.
3. C. The XGBoost model provides a true/false binary outpu
4. D. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.
5. E. The XGBoost model is a supervised machine learning algorith
6. F. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.
7. G. The XGBoost model is a supervised machine learning algorith
8. H. Train a k-means model with K = 5 on the same dataset to predict a segment.

Correct Answer: C

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

1. A. Recall
2. B. Misclassification rate
3. C. Mean absolute percentage error (MAPE)
4. D. Area Under the ROC Curve (AUC)

Correct Answer: D

A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions
What metric is BEST suited to score the model?

1. A. Precision
2. B. Recall
3. C. Area Under the ROC Curve (AUC)
4. D. Root Mean Square Error (RMSE)

Correct Answer: A

A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

1. A. Associate the Amazon SageMaker notebook with a private subnet in a VP
2. B. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
3. C. Associate the Amazon SageMaker notebook with a private subnet in a VP
4. D. Use 1AM policies to grant access to Amazon S3 and Amazon SageMaker.
5. E. Associate the Amazon SageMaker notebook with a private subnet in a VP
6. F. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
7. G. Associate the Amazon SageMaker notebook with a private subnet in a VP
8. H. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker

Correct Answer: D