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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q160-Q165):

NEW QUESTION # 160
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

• A. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
• B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
• C. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
• D. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
• E. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Answer: A,C

NEW QUESTION # 161
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)

• A. Increase feature combinations.
• B. Decrease regularization.
• C. Decrease dropout.
• D. Increase regularization.
• E. Increase dropout.
• F. Decrease feature combinations.

Answer: D,E,F

Explanation:
The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:
* Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.
* Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.
* Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.
1: Regularization for Deep Learning - Amazon SageMaker
2: Dropout - Amazon SageMaker
3: Feature Engineering - Amazon SageMaker

NEW QUESTION # 162
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.

Answer: B

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and language. Amazon Comprehend also provides custom classification and custom entity recognition features that allow users to train their own models using their own data and labels.
For the scenario of routing customer claims to different queues based on categories, Amazon Comprehend custom classification is a suitable solution. The custom classifier can be trained using a .csv file that contains the claim text and the claim label as columns. The custom classifier can then be used to process incoming claims and predict the labels using the Amazon Comprehend API. The predicted labels can be used to route the claims to the appropriate queue. This solution does not require any machine learning expertise or model deployment, and it can be easily integrated with the existing application.
The other options are not suitable because:
Option A: Amazon SageMaker Object2Vec is an algorithm that can learn embeddings of objects such as words, sentences, or documents. It can be used for tasks such as text classification, sentiment analysis, or recommendation systems. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company.
Option B: Amazon SageMaker Latent Dirichlet Allocation (LDA) is an algorithm that can discover the topics or themes in a collection of documents. It can be used for tasks such as topic modeling, document clustering, or text summarization. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company. Moreover, LDA does not provide labels for the topics, but rather a distribution of words for each topic, which may not match the existing categories of the claims.
Option C: Amazon Textract is a service that can extract text and data from scanned documents or images. It can be used for tasks such as document analysis, data extraction, or form processing.
However, using this service is unnecessary and inefficient for the scenario, since the company already has the claim text and label in a database. Moreover, Amazon Textract does not provide custom classification features, so it cannot be used to train a custom classifier using the existing data and labels.
References:
Amazon Comprehend Custom Classification
Amazon SageMaker Object2Vec
Amazon SageMaker Latent Dirichlet Allocation
Amazon Textract

NEW QUESTION # 163
A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

• A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
• B. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.
• C. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset.
  Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
• D. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.

Answer: B

Explanation:
The solution D meets the requirements with the least operational overhead because it uses Amazon SageMaker Autopilot, which is a fully managed service that automates the end-to-end process of building, training, and deploying machine learning models. Amazon SageMaker Autopilot can handle data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model deployment. The company only needs to create an IAM role for Amazon SageMaker with access to the S3 bucket, create a SageMaker AutoML job pointing to the bucket with the dataset, specify the price as the target attribute, and wait for the job to complete. Amazon SageMaker Autopilot will generate a list of candidate models with different configurations and performance metrics, and the company can deploy the best model for predictions1.
The other options are not suitable because:
* Option A: Creating a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket, creating an ECS cluster based on an AWS Deep Learning Containers image, writing the code to perform the feature engineering, training a logistic regression model for predicting the price, and performing the inferences will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to manage the ECS cluster, the container image, the code, the model, and the inference endpoint. Moreover, logistic regression may not be the best algorithm for predicting the price, as it is more suitable for binary classification tasks2.
* Option B: Creating an Amazon SageMaker notebook with a new IAM role that is associated with the notebook, pulling the dataset from the S3 bucket, exploring different combinations of feature engineering transformations, regression algorithms, and hyperparameters, comparing all the results in the notebook, and deploying the most accurate configuration in an endpoint for predictions will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to write the code for the feature engineering, the model training, the model evaluation, and the model deployment. The company will also have to manually compare the results and select the best configuration3.
* Option C: Creating an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda, creating a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset, specifying the price as the target feature, loading the model artifact to a Lambda function for inference on prices of new houses will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to create and manage the Lambda function, the model artifact, and the inference endpoint. Moreover, XGBoost may not be the best algorithm for predicting the price, as it is more suitable for classification and ranking tasks4.
1: Amazon SageMaker Autopilot
2: Amazon Elastic Container Service
3: Amazon SageMaker Notebook Instances
4: Amazon SageMaker XGBoost Algorithm

NEW QUESTION # 164
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. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
• B. Support vector machine (SVM) with non-linear kernel
• C. Single perceptron with tanh activation function
• D. Logistic regression

Answer: B

Explanation:
Based on the figure provided, the data is not linearly separable. Therefore, a non-linear model such as SVM with a non-linear kernel would be the best choice. SVMs are particularly effective in high-dimensional spaces and are versatile in that they can be used for both linear and non-linear data. Additionally, SVMs have a high level of accuracy and are less prone to overfitting1 References: 1: https://docs.aws.amazon.com/sagemaker/latest/dg/svm.html

NEW QUESTION # 165
......

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