MLS-C01 TEST TORRENT & MLS-C01 LEARNING MATERIALS & MLS-C01 DUMPS VCE

MLS-C01 Test Torrent & MLS-C01 Learning Materials & MLS-C01 Dumps VCE

MLS-C01 Test Torrent & MLS-C01 Learning Materials & MLS-C01 Dumps VCE

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To be eligible to take the exam, candidates should have at least one year of experience in designing and implementing machine learning solutions on AWS, as well as a strong understanding of core AWS services and the ability to use them to solve real-world problems. AWS Certified Machine Learning - Specialty certification exam is intended for professionals who work in roles such as data scientists, data analysts, machine learning engineers, and developers who want to specialize in machine learning. Achieving this certification can help professionals boost their careers, as it demonstrates to employers and clients that they have the necessary skills and knowledge to design and implement machine learning solutions on AWS.

The AWS Certified Machine Learning - Specialty certification exam covers various topics related to ML, such as data preparation, feature engineering, model training and evaluation, deployment and implementation, and machine learning algorithms. MLS-C01 Exam is designed to test the candidate’s ability to design and implement ML models using Amazon SageMaker, understand the core ML concepts, and select the appropriate AWS services to deploy ML solutions. Additionally, the exam also tests the candidate’s ability to optimize and tune ML models to achieve the desired outcome.

The AWS Certified Machine Learning - Specialty exam tests candidates on a variety of topics, including data preparation, feature engineering, model selection and evaluation, machine learning algorithms, and deployment and implementation of machine learning solutions on AWS. MLS-C01 exam also covers topics related to AWS machine learning services, such as Amazon SageMaker, Amazon Comprehend, and Amazon Rekognition.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q156-Q161):

NEW QUESTION # 156
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 Amazon Textract. 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 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.
  • C. 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.
  • D. 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.

Answer: A

Explanation:
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


NEW QUESTION # 157
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

  • A. Area Under the ROC Curve (AUC)
  • B. Recall
  • C. Mean absolute percentage error (MAPE)
  • D. Misclassification rate

Answer: B


NEW QUESTION # 158
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes.
What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  • A. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  • B. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
  • C. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  • D. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Answer: B

Explanation:
Explanation


NEW QUESTION # 159
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

  • A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • B. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • C. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
  • D. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Answer: B


NEW QUESTION # 160
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
* True positive rate (TPR): 0.700
* False negative rate (FNR): 0.300
* True negative rate (TNR): 0.977
* False positive rate (FPR): 0.023
* Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?

  • A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
  • B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
  • C. Oversample the majority class.
  • D. Undersample the minority class.

Answer: B

Explanation:
The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset, and retrain the model with the updated training data. This solution can address the problem of class imbalance in the dataset, which can affect the model's ability to learn from the rare but important positive class (fraud).
Class imbalance is a common issue in machine learning, especially for classification tasks. It occurs when one class (usually the positive or target class) is significantly underrepresented in the dataset compared to the other class (usually the negative or non-target class). For example, in the credit card fraud detection problem, the positive class (fraud) is much less frequent than the negative class (fair transactions). This can cause the model to be biased towards the majority class, and fail to capture the characteristics and patterns of the minority class. As a result, the model may have a high overall accuracy, but a low recall or true positive rate for the minority class, which means it misses many fraudulent transactions.
SMOTE is a technique that can help mitigate the class imbalance problem by generating synthetic samples for the minority class. SMOTE works by finding the k-nearest neighbors of each minority class instance, and randomly creating new instances along the line segments connecting them. This way, SMOTE can increase the number and diversity of the minority class instances, without duplicating or losing any information. By applying SMOTE on the minority class in the training dataset, the data scientist can balance the classes and improve the model's performance on the positive class1.
The other options are either ineffective or counterproductive. Applying SMOTE on the majority class would not balance the classes, but increase the imbalance and the size of the dataset. Undersampling the minority class would reduce the number of instances available for the model to learn from, and potentially lose some important information. Oversampling the majority class would also increase the imbalance and the size of the dataset, and introduce redundancy and overfitting.
References:
1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery


NEW QUESTION # 161
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