Online Access Free Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-11-29.q177 Practice Test (Page 31)

AWS-Certified-Machine-Learning-Specialty Exam Question 146

A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has
15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A.A DeepAR forecasting model based on a recurrent neural network (RNN)

B.An XGBoost model where the objective parameter is set to multi: softmax

C.A deep convolutional neural network (CNN) with a softmax activation function for the last layer

D.A regression forest where the number of trees is set equal to the number of product categories

AWS-Certified-Machine-Learning-Specialty Exam Question 147

A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A.Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

B.Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

C.Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

D.Use an 1AM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

AWS-Certified-Machine-Learning-Specialty Exam Question 148

A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models

A.Initialize the model with random weights in all layers including the last fully connected layer

B.Initialize the model with pre-trained weights in all layers including the last fully connected layer

C.Initialize the model with pre-trained weights in all layers and replace the last fully connected layer.

D.Initialize the model with random weights in all layers and replace the last fully connected layer

AWS-Certified-Machine-Learning-Specialty Exam Question 149

A bank wants to launch a low-rate credit promotion. The bank is located in a town that recently experienced economic hardship. Only some of the bank's customers were affected by the crisis, so the bank's credit team must identify which customers to target with the promotion. However, the credit team wants to make sure that loyal customers' full credit history is considered when the decision is made.
The bank's data science team developed a model that classifies account transactions and understands credit eligibility. The data science team used the XGBoost algorithm to train the model. The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days.
The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers. The credit team has almost no skill in data science.
What should the data science team do to address this issue in the MOST operationally efficient manner?

A.Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Enable Amazon SageMaker Model Monitor to store inferences. Use the inferences to create Shapley values that help explain model behavior. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.

B.Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Activate Amazon SageMaker Debugger, and configure it to calculate and collect Shapley values. Create a chart that shows features and SHapley Additive exPlanations (SHAP) values to explain to the credit team how the features affect the model outcomes.

C.Create an Amazon SageMaker notebook instance. Use the notebook instance and the XGBoost library to locally retrain the model. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart. Use that chart to explain to the credit team how the features affect the model outcomes.

D.Use Amazon SageMaker Studio to rebuild the model. Create a notebook that uses the XGBoost training container to perform model training. Deploy the model at an endpoint. Use Amazon SageMaker

AWS-Certified-Machine-Learning-Specialty Exam Question 150

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 accuracy?

A.Single perceptron with tanh activation function

B.Logistic regression

C.Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)

D.Support vector machine (SVM) with non-linear kernel

Other Version: 1671Amazon.AWS-Certified-Machine-Learning-Specialty.v2025-02-06.q184; 2088Amazon.AWS-Certified-Machine-Learning-Specialty.v2024-02-17.q264; 64Amazon.Vceengine.AWS-Certified-Machine-Learning-Specialty.v2022-03-05.by.kelly.115q.pdf; 3680Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-01-17.q115; 2949Amazon.AWS-Certified-Machine-Learning-Specialty.v2022-01-01.q86; 46Amazon.Actualvce.AWS-Certified-Machine-Learning-Specialty.v2021-08-11.by.adonis.87q.pdf

Latest Upload: 227Amazon.SAA-C03.v2026-07-25.q425; 129SAP.C_S4PM.v2026-07-25.q43; 223Google.Professional-Data-Engineer.v2026-07-25.q215; 219Microsoft.AZ-104.v2026-07-25.q227; 187Amazon.DOP-C02.v2026-07-25.q201; 153EMC.NCP-AII.v2026-07-25.q60; 152CheckPoint.156-561.v2026-07-25.q83; 229Amazon.AIF-C01.v2026-07-24.q158; 163WGU.Applied-Algebra.v2026-07-24.q46; 159Oracle.1Z0-948.v2026-07-24.q60

AWS-Certified-Machine-Learning-Specialty Exam Question 146

AWS-Certified-Machine-Learning-Specialty Exam Question 147

AWS-Certified-Machine-Learning-Specialty Exam Question 148

AWS-Certified-Machine-Learning-Specialty Exam Question 149

AWS-Certified-Machine-Learning-Specialty Exam Question 150

Download PDF File