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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is an industry-recognized certification that validates your expertise in designing, deploying, and maintaining machine learning solutions on the Amazon Web Services platform. It is designed for professionals who want to demonstrate their ability to use AWS services to build and deploy machine learning models.

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The AWS-Certified-Machine-Learning-Specialty exam is a challenging certification exam aimed at validating the skills and knowledge of individuals who want to design, implement, and deploy machine learning solutions using AWS services. Candidates who Pass MLS-C01 Exam can demonstrate their expertise in machine learning and can enhance their career prospects in the field of data science and machine learning.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q291-Q296):

NEW QUESTION # 291
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.
Which solution will meet these requirements with the LEAST development effort?

A. Use an Amazon Personalize USER_ PERSONAL IZATION recipe to train a model Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.
B. Train a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.
C. Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.
D. Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.

Answer: A

Explanation:
Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:
Amazon Personalize
What is Amazon Personalize?
USER_PERSONALIZATION recipe
PERSONALIZED_RANKING recipe
Filtering recommendations
GetRecommendations API operation

NEW QUESTION # 292
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 needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

A. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
B. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
E. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).

Answer: C,E

Explanation:
Explanation
The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.
The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non-fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.
The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.
References:
XGBoost Parameters - Amazon Machine Learning
Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

NEW QUESTION # 293
A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.
Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

A. Enable network isolation for training jobs and models.
B. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
C. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.
D. Use SCPs to restrict access to SageMaker.
E. Restrict notebook presigned URLs to specific IPs used by the company.
F. Disable root access on the SageMaker notebook instances.

Answer: A,B,C

Explanation:
To control data egress from SageMaker, the ML engineer can use the following mechanisms:
* Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink. This allows the ML engineer to access SageMaker services and resources without exposing the traffic to the public internet. This reduces the risk of data leakage and unauthorized access1
* Enable network isolation for training jobs and models. This prevents the training jobs and models from accessing the internet or other AWS services. This ensures that the data used for training and inference is not exposed to external sources2
* Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys. This enables the ML engineer to encrypt the data stored in Amazon S3 buckets, SageMaker notebook instances, and SageMaker endpoints. It also allows the ML engineer to encrypt the data in transit between SageMaker and other AWS services. This helps protect the data from unauthorized access and tampering3 The other options are not effective in controlling data egress from SageMaker:
* Use SCPs to restrict access to SageMaker. SCPs are used to define the maximum permissions for an organization or organizational unit (OU) in AWS Organizations. They do not control the data egress from SageMaker, but rather the access to SageMaker itself4
* Disable root access on the SageMaker notebook instances. This prevents the users from installing additional packages or libraries on the notebook instances. It does not prevent the data from being transferred out of the notebook instances.
* Restrict notebook presigned URLs to specific IPs used by the company. This limits the access to the notebook instances from certain IP addresses. It does not prevent the data from being transferred out of the notebook instances.
1: Amazon SageMaker Interface VPC Endpoints (AWS PrivateLink) - Amazon SageMaker
2: Network Isolation - Amazon SageMaker
3: Encrypt Data at Rest and in Transit - Amazon SageMaker
4: Using Service Control Policies - AWS Organizations
Disable Root Access - Amazon SageMaker
Create a Presigned Notebook Instance URL - Amazon SageMaker

NEW QUESTION # 294
A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues.
The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset.
Which feature engineering technique should the Data Scientist use to meet the objectives?

A. Normalize all numerical values to be between 0 and 1
B. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
C. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
D. Run self-correlation on all features and remove highly correlated features

Answer: C

Explanation:
The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Dimensionality Reduction for Machine Learning
* AWS Machine Learning Training - Deep Learning with Amazon SageMaker

NEW QUESTION # 295
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?

A. An Amazon S3 data lake with hourly object prefixes
B. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
C. An Amazon RDS database with hourly table partitions
D. An Amazon EBS-backed Amazon EC2 instance with hourly directories

Answer: A

Explanation:
An Amazon S3 data lake is a cost-effective solution for storing and analyzing large amounts of time-based training data for a new model. Amazon S3 is a highly scalable, durable, and secure object storage service that can store any amount of data in any format. Amazon S3 also offers low-cost storage classes, such as S3 Standard-IA and S3 One Zone-IA, that can reduce the storage costs for infrequently accessed data. By using hourly object prefixes, the Machine Learning Specialist can organize the data into logical partitions based on the time of ingestion. This can enable efficient data access and management, as well as support incremental updates and deletes. The Specialist can also use Amazon S3 lifecycle policies to automatically transition the data to lower-cost storage classes or delete the data after a certain period of time. This way, the Specialist can always train on the last 24 hours of the data and optimize the storage costs.
References:
What is a data lake? - Amazon Web Services
Amazon S3 Storage Classes - Amazon Simple Storage Service
Managing your storage lifecycle - Amazon Simple Storage Service
Best Practices Design Patterns: Optimizing Amazon S3 Performance

NEW QUESTION # 296
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