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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q77-Q82):

NEW QUESTION # 77
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?

A. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
C. Use Amazon Comprehend with the transcribed files to build the key topics
D. Use Amazon Translate with the transcribed files to train and build a model for the key topics

Answer: C

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:
Amazon Comprehend - Amazon Web Services
AWS Certified Machine Learning - Specialty Sample Questions

NEW QUESTION # 78
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. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
B. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
D. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
E. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Answer: C,E

Explanation:
The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).
The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.
The XGBoost algorithm can also use different evaluation metrics to optimize the model performance. The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives. A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.
Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.
XGBoost Parameters
XGBoost for Imbalanced Classification

NEW QUESTION # 79
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

A. Apply the Pearson correlation coefficient
B. Use matrix multiplication on highly correlated features.
C. Perform one-hot encoding on highly correlated features
D. Create a new feature space using principal component analysis (PCA)

Answer: D

Explanation:
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible.
This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:
* Principal Component Analysis (PCA) Algorithm - Amazon SageMaker
* Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog
* Machine Learning- Prinicipal Component Analysis | i2tutorials

NEW QUESTION # 80
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
C. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
D. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.

Answer: D

Explanation:
Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance's EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases

NEW QUESTION # 81
A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort?

A. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.
B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
C. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.
D. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway.
Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.

Answer: C

Explanation:
In this scenario, the company requires that access to the Amazon SageMaker notebook remain within the AWS internal network, avoiding the public internet. By creating a VPC interface endpoint for SageMaker, the company can ensure that traffic to the SageMaker notebook remains internal to the VPC and is accessible over a private connection. The VPC interface endpoint allows private network access to AWS services, and it operates over AWS's internal network, respecting the security and connectivity policies the company requires.
This solution requires minimal development effort compared to options involving bastion hosts or NAT gateways, as it directly provides private network access to the SageMaker notebook.

NEW QUESTION # 82
......

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