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MLS-C01완벽한덤프자료 & MLS-C01시험대비덤프데모

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>> MLS-C01완벽한 덤프자료 <<

MLS-C01시험대비 덤프데모 & MLS-C01덤프문제집

Itexamdump이 바로 아주 좋은Amazon MLS-C01인증시험덤프를 제공할 수 있는 사이트입니다. Itexamdump 의 덤프자료는 IT관련지식이 없는 혹은 적은 분들이 고난의도인Amazon MLS-C01인증시험을 패스할 수 있습니다. 만약Itexamdump에서 제공하는Amazon MLS-C01인증시험덤프를 장바구니에 넣는다면 여러분은 많은 시간과 정신력을 절약하실 수 있습니다. 우리Itexamdump 의Amazon MLS-C01인증시험덤프는 Itexamdump전문적으로Amazon MLS-C01인증시험대비로 만들어진 최고의 자료입니다.

최신 AWS Certified Specialty MLS-C01 무료샘플문제 (Q87-Q92):

질문 # 87
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizz a. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

A. Misclassification rate
B. Receiver operating characteristic (ROC) curve
C. Root Mean Square Error (RM&)
D. L1 norm

정답：B

설명：
A receiver operating characteristic (ROC) curve is a model evaluation technique that can be used to understand how different classification thresholds will impact the model's performance. A ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) for various values of the classification threshold. The TPR, also known as sensitivity or recall, is the proportion of positive instances that are correctly classified as positive. The FPR, also known as the fall-out, is the proportion of negative instances that are incorrectly classified as positive. A ROC curve can show the trade-off between the TPR and the FPR for different thresholds, and help the Machine Learning Specialist to select the optimal threshold that maximizes the TPR and minimizes the FPR. A ROC curve can also be used to compare the performance of different models by calculating the area under the curve (AUC), which is a measure of how well the model can distinguish between the positive and negative classes. A higher AUC indicates a better model

질문 # 88
A machine learning (ML) specialist is building a credit score model for a financial institution. The ML specialist has collected data for the previous 3 years of transactions and third-party metadata that is related to the transactions.
After the ML specialist builds the initial model, the ML specialist discovers that the model has low accuracy for both the training data and the test data. The ML specialist needs to improve the accuracy of the model.
Which solutions will meet this requirement? (Select TWO.)

A. Use fewer feature combinations. Decrease the number of numeric attribute bins.
B. Decrease the amount of training data examples. Reduce the number of passes on the existing training data.
C. Increase the number of passes on the existing training data. Perform more hyperparameter tuning.
D. Add new domain-specific features. Use more complex models.
E. Increase the amount of regularization. Use fewer feature combinations.

정답：C,D

설명：
For a model with low accuracy on both training and testing datasets, the following two strategies are effective:
* Increase the number of passes and perform hyperparameter tuning: This approach allows the model to better learn from the existing data and improve performance through optimized hyperparameters.
* Add domain-specific features and use more complex models: Adding relevant features that capture additional information from domain knowledge and using more complex model architectures can help the model capture patterns better, potentially improving accuracy.
Options B, D, and E would either reduce feature complexity or training data volume, which is less likely to improve performance when accuracy is low on both training and testing sets.

질문 # 89
A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

A. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
B. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
C. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
D. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.

정답：B

설명：
The best solution for text extraction and entity detection with the least amount of effort is to use Amazon Textract and Amazon Comprehend. These services are:
Amazon Textract for text extraction from receipt images. Amazon Textract is a machine learning service that can automatically extract text and data from scanned documents. It can handle different structures and formats of documents, such as PDF, TIFF, PNG, and JPEG, without any preprocessing steps. It can also extract key-value pairs and tables from documents1 Amazon Comprehend for entity detection and custom entity detection. Amazon Comprehend is a natural language processing service that can identify entities, such as dates, locations, and notes, from unstructured text. It can also detect custom entities, such as receipt numbers, by using a custom entity recognizer that can be trained with a small amount of labeled data2 The other options are not suitable because they either require more effort for text extraction, entity detection, or custom entity detection. For example:
Option A uses the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. BlazingText is a supervised learning algorithm that can perform text classification and word2vec. It requires users to provide a large amount of labeled data, preprocess the data into a specific format, and tune the hyperparameters of the model3 Option B uses a deep learning OCR model from the AWS Marketplace and a NER deep learning model for text extraction and entity detection. These models are pre-trained and may not be suitable for the specific use case of receipt processing. They also require users to deploy and manage the models on Amazon SageMaker or Amazon EC2 instances4 Option D uses a deep learning OCR model from the AWS Marketplace for text extraction. This model has the same drawbacks as option B. It also requires users to integrate the model output with Amazon Comprehend for entity detection and custom entity detection.
References:
1: Amazon Textract - Extract text and data from documents
2: Amazon Comprehend - Natural Language Processing (NLP) and Machine Learning (ML)
3: BlazingText - Amazon SageMaker
4: AWS Marketplace: OCR

질문 # 90
A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social medi a. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages. The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?

A. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.
B. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
C. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
D. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.

정답：C

질문 # 91
A data scientist is training a text classification model by using the Amazon SageMaker built-in BlazingText algorithm. There are 5 classes in the dataset, with 300 samples for category A, 292 samples for category B,
240 samples for category C, 258 samples for category D, and 310 samples for category E.
The data scientist shuffles the data and splits off 10% for testing. After training the model, the data scientist generates confusion matrices for the training and test sets.

What could the data scientist conclude form these results?

A. The data distribution is skewed.
B. Classes C and D are too similar.
C. The dataset is too small for holdout cross-validation.
D. The model is overfitting for classes B and E.

정답：D

설명：
Explanation
A confusion matrix is a matrix that summarizes the performance of a machine learning model on a set of test data. It displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) produced by the model on the test data1. For multi-class classification, the matrix shape will be equal to the number of classes i.e for n classes it will be nXn1. The diagonal values represent the number of correct predictions for each class, and the off-diagonal values represent the number of incorrect predictions for each class1.
The BlazingText algorithm is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). BlazingText works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values2.
From the confusion matrices for the training and test sets, we can observe the following:
The model has a high accuracy on the training set, as most of the diagonal values are high and the off-diagonal values are low. This means that the model is able to learn the patterns and features of the training data well.
However, the model has a lower accuracy on the test set, as some of the diagonal values are lower and some of the off-diagonal values are higher. This means that the model is not able to generalize well to the unseen data and makes more errors.
The model has a particularly high error rate for classes B and E on the test set, as the values of M_22 and M_55 are much lower than the values of M_12, M_21, M_15, M_25, M_51, and M_52. This means that the model is confusing classes B and E with other classes more often than it should.
The model has a relatively low error rate for classes A, C, and D on the test set, as the values of M_11, M_33, and M_44 are high and the values of M_13, M_14, M_23, M_24, M_31, M_32, M_34, M_41, M_42, and M_43 are low. This means that the model is able to distinguish classes A, C, and D from other classes well.
These results indicate that the model is overfitting for classes B and E, meaning that it is memorizing the specific features of these classes in the training data, but failing to capture the general features that are applicable to the test data. Overfitting is a common problem in machine learning, where the model performs well on the training data, but poorly on the test data3. Some possible causes of overfitting are:
The model is too complex or has too many parameters for the given data. This makes the model flexible enough to fit the noise and outliers in the training data, but reduces its ability to generalize to new data.
The data is too small or not representative of the population. This makes the model learn from a limited or biased sample of data, but fails to capture the variability and diversity of the population.
The data is imbalanced or skewed. This makes the model learn from a disproportionate or uneven distribution of data, but fails to account for the minority or rare classes.
Some possible solutions to prevent or reduce overfitting are:
Simplify the model or use regularization techniques. This reduces the complexity or the number of parameters of the model, and prevents it from fitting the noise and outliers in the data. Regularization techniques, such as L1 or L2 regularization, add a penalty term to the loss function of the model, which shrinks the weights of the model and reduces overfitting3.
Increase the size or diversity of the data. This provides more information and examples for the model to learn from, and increases its ability to generalize to new data. Data augmentation techniques, such as rotation, flipping, cropping, or noise addition, can generate new data from the existing data by applying some transformations3.
Balance or resample the data. This adjusts the distribution or the frequency of the data, and ensures that the model learns from all classes equally. Resampling techniques, such as oversampling or undersampling, can create a balanced dataset by increasing or decreasing the number of samples for each class3.
References:
Confusion Matrix in Machine Learning - GeeksforGeeks
BlazingText algorithm - Amazon SageMaker
Overfitting and Underfitting in Machine Learning - GeeksforGeeks

질문 # 92
......

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