A: Build a custom clustering model. Create a Dockerfile and build a Docker image. Register the Docker image in Amazon Elastic Container Registry (Amazon ECR). Use the custom image in Amazon SageMaker to generate a trained model.

B: Tokenize the data and transform the data into tabulai data. Train an Amazon SageMaker k-means mode to generate the product categories.

C: Train an Amazon SageMaker Neural Topic Model (NTM) model to generate the product categories.

D: Train an Amazon SageMaker Blazing Text model to generate the product categories.

Answer

Correct Answer: C

Q2: A business to business (B2B) ecommerce company wants to develop a fair and equitable risk mitigation strategy to reject potentially fraudulent transactions. The company wants to reject fraudulent transactions despite the possibility of losing some profitable transactions or customers. Which solution will meet these requirements with the LEAST operational effort?

A: Use Amazon SageMaker to approve transactions only for products the company has sold in the past.

B: Use Amazon SageMaker to train a custom fraud detection model based on customer data.

C: Use the Amazon Fraud Detector prediction API to approve or deny any activities that Fraud Detector identifies as fraudulent.

D: Use the Amazon Fraud Detector prediction API to identify potentially fraudulent activities so the company can review the activities and reject fraudulent transactions.

Answer

Correct Answer: C

Q3: An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset. Which approach should the ML specialist use to improve the performance of the model on the testing data?

A: Increase the value of the momentum hyperparameter.

B: Reduce the value of the dropout_rate hyperparameter.

C: Reduce the value of the learning_rate hyperparameter.

D: Increase the value of the L2 hyperparameter.

Answer

Correct Answer: D

Q4: An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute. How should the data scientist meet these requirements MOST cost-effectively?

A: Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:accuracy', 'Type': 'Maximize'}}

B: Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

C: Tune all possible hyperparameters by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Maximize'}}.

D: Tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {'HyperParameterTuningJobObjective': {'MetricName': 'validation:f1', 'Type': 'Minimize'}).

Answer

Correct Answer: B

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Q1: A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products. Which solution will meet these requirements with the MOST operational efficiency?

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