Amazon AWS Certified Machine Learning - Specialty
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#121 (Accuracy: 100% / 4 votes)
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 data. 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 SageMaker built-in transient keys to encrypt the ML data volumes. Enable default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes.
  • 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 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.
#122 (Accuracy: 100% / 3 votes)
A company hosts a public web application on AWS. The application provides a user feedback feature that consists of free-text fields where users can submit text to provide feedback. The company receives a large amount of free-text user feedback from the online web application. The product managers at the company classify the feedback into a set of fixed categories including user interface issues, performance issues, new feature request, and chat issues for further actions by the company's engineering teams.

A machine learning (ML) engineer at the company must automate the classification of new user feedback into these fixed categories by using Amazon SageMaker. A large set of accurate data is available from the historical user feedback that the product managers previously classified.

Which solution should the ML engineer apply to perform multi-class text classification of the user feedback?
  • A. Use the SageMaker Latent Dirichlet Allocation (LDA) algorithm.
  • B. Use the SageMaker BlazingText algorithm.
  • C. Use the SageMaker Neural Topic Model (NTM) algorithm.
  • D. Use the SageMaker CatBoost algorithm.
#123 (Accuracy: 100% / 2 votes)
A company is building a new supervised classification model in an AWS environment. The company's data science team notices that the dataset has a large quantity of variables. All the variables are numeric.

The model accuracy for training and validation is low. The model's processing time is affected by high latency. The data science team needs to increase the accuracy of the model and decrease the processing time.

What should the data science team do to meet these requirements?
  • A. Create new features and interaction variables.
  • B. Use a principal component analysis (PCA) model.
  • C. Apply normalization on the feature set.
  • D. Use a multiple correspondence analysis (MCA) model.
#124 (Accuracy: 100% / 3 votes)
A company's machine learning (ML) specialist is building a computer vision model to classify 10 different traffic signs. The company has stored 100 images of each class in Amazon S3, and the company has another 10,000 unlabeled images. All the images come from dash cameras and are a size of 224 pixels × 224 pixels. After several training runs, the model is overfitting on the training data.

Which actions should the ML specialist take to address this problem? (Choose two.)
  • A. Use Amazon SageMaker Ground Truth to label the unlabeled images.
  • B. Use image preprocessing to transform the images into grayscale images.
  • C. Use data augmentation to rotate and translate the labeled images.
  • D. Replace the activation of the last layer with a sigmoid.
  • E. Use the Amazon SageMaker k-nearest neighbors (k-NN) algorithm to label the unlabeled images.
#125 (Accuracy: 100% / 8 votes)
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)
  • A. Data scaling with standardization and normalization
  • B. Correlation plot with heat maps
  • C. Data binning
  • D. Univariate selection
  • E. Feature importance with a tree-based classifier
  • F. Data augmentation
#126 (Accuracy: 100% / 3 votes)
A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.

Which solution will meet these requirements?
  • A. Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.
  • B. Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook’s VPC.
  • C. Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the IAM role.
  • D. Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.
#127 (Accuracy: 95% / 9 votes)
A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team's needs? (Choose two.)
  • A. Add L1 regularization to the classifier
  • B. Add features to the dataset
  • C. Perform recursive feature elimination
  • D. Perform t-distributed stochastic neighbor embedding (t-SNE)
  • E. Perform linear discriminant analysis
#128 (Accuracy: 100% / 4 votes)
An analytics company has an Amazon SageMaker hosted endpoint for an image classification model. The model is a custom-built convolutional neural network (CNN) and uses the PyTorch deep learning framework. The company wants to increase throughput and decrease latency for customers that use the model.

Which solution will meet these requirements MOST cost-effectively?
  • A. Use Amazon Elastic Inference on the SageMaker hosted endpoint.
  • B. Retrain the CNN with more layers and a larger dataset.
  • C. Retrain the CNN with more layers and a smaller dataset.
  • D. Choose a SageMaker instance type that has multiple GPUs.
#129 (Accuracy: 100% / 4 votes)
A company has a podcast platform that has thousands of users. The company implemented an algorithm to detect low podcast engagement based on a 10-minute running window of user events such as listening to, pausing, and closing the podcast. A machine learning (ML) specialist is designing the ingestion process for these events. The ML specialist needs to transform the data to prepare the data for inference.

How should the ML specialist design the transformation step to meet these requirements with the LEAST operational effort?
  • A. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
  • B. Use Amazon Kinesis Data Streams to ingest event data. Store the data in Amazon S3 by using Amazon Kinesis Data Firehose. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
  • C. Use Amazon Kinesis Data Streams to ingest event data. Use Amazon Kinesis Data Analytics to transform the most recent 10 minutes of data before inference.
  • D. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster to ingest event data. Use AWS Lambda to transform the most recent 10 minutes of data before inference.
#130 (Accuracy: 100% / 5 votes)
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.

A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.

Which algorithms are best suited to this scenario? (Choose two.)
  • A. Latent Dirichlet allocation (LDA)
  • B. Random forest classifier
  • C. Neural topic modeling (NTM)
  • D. Linear support vector machine
  • E. Linear regression