Instant Access Amazon.AWS-Certified-Machine-Learning-Specialty.v2026-04-30.q178 Actual Practice Test Engine for Free (Page 28)

Question 131

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

A.Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
B.Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
C.Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
D.Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Question 132

A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target.
What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

A.Area under the curve
B.Root Mean Square Error (RMSE)
C.Residual plots
D.Confusion matrix

Question 133

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.
Which storage option provides the most processing flexibility and will allow access control with IAM?

A.Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
B.Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
C.Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
D.Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Question 134

An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining.
How should a machine learning specialist meet these requirements?

A.Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
B.Create an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site- to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
C.Use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
D.Use PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.

Question 135

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? (Select 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.

Correct Answer: C,E

Data augmentation is a technique to increase the size and diversity of the training data by applying random transformations such as rotation, translation, scaling, flipping, etc. This can help reduce overfitting and improve the generalization of the model. Data augmentation can be done using the Amazon SageMaker image classification algorithm, which supports various augmentation options such as horizontal_flip, vertical_flip, rotate, brightness, contrast, etc1 The Amazon SageMaker k-nearest neighbors (k-NN) algorithm is a supervised learning algorithm that can be used to label unlabeled data based on the similarity to the labeled data. The k-NN algorithm assigns a label to an unlabeled instance by finding the k closest labeled instances in the feature space and taking a majority vote among their labels. This can help increase the size and diversity of the training data and reduce overfitting. The k-NN algorithm can be used with the Amazon SageMaker image classification algorithm by extracting features from the images using a pre-trained model and then applying the k-NN algorithm on the feature vectors2 Using Amazon SageMaker Ground Truth to label the unlabeled images is not a good option because it is a manual and costly process that requires human annotators. Moreover, it does not address the issue of overfitting on the existing labeled data.
Using image preprocessing to transform the images into grayscale images is not a good option because it reduces the amount of information and variation in the images, which can degrade the performance of the model. Moreover, it does not address the issue of overfitting on the existing labeled data.
Replacing the activation of the last layer with a sigmoid is not a good option because it is not suitable for a multi-class classification problem. A sigmoid activation function outputs a value between 0 and 1, which can be interpreted as a probability of belonging to a single class. However, for a multi-class classification problem, the output should be a vector of probabilities that sum up to 1, which can be achieved by using a softmax activation function.
References:
1: Image classification algorithm - Amazon SageMaker
2: k-nearest neighbors (k-NN) algorithm - Amazon SageMaker

Question 131

Question 132

Question 133

Question 134

Question 135

Download PDF File