VALID MLS-C01 EXAM NOTES - EXAM CRAM MLS-C01 PDF

Valid MLS-C01 Exam Notes - Exam Cram MLS-C01 Pdf

Valid MLS-C01 Exam Notes - Exam Cram MLS-C01 Pdf

Blog Article

Tags: Valid MLS-C01 Exam Notes, Exam Cram MLS-C01 Pdf, Latest Test MLS-C01 Simulations, Latest MLS-C01 Dumps Book, MLS-C01 Valid Test Camp

P.S. Free & New MLS-C01 dumps are available on Google Drive shared by PassCollection: https://drive.google.com/open?id=1A7bNMD4PNYB8xe6Mo6Ba6gZtxY6lx8u9

With MLS-C01 actual exam engine you will experience an evolution of products coupled with the experience and qualities of expertise. All the questions of MLS-C01 free pdf are checked chosen by several times of refining and verification, and all the MLS-C01 answers are correct and easy to understand. You can experience yourself a new dawn of technology with MLS-C01 exam torrent. We guarantee you 100% pass. If you are still worried, you can read our refund policy. In case of failure, full refund.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed for individuals who have a strong understanding of machine learning concepts, techniques, and best practices. MLS-C01 exam is intended to validate an individual's technical expertise in building and deploying machine learning models on the AWS platform. AWS Certified Machine Learning - Specialty certification is suitable for anyone working with machine learning technologies, including data scientists, developers, and software engineers.

>> Valid MLS-C01 Exam Notes <<

Exam Cram MLS-C01 Pdf & Latest Test MLS-C01 Simulations

You choosing PassCollection to help you pass Amazon certification MLS-C01 exam is a wise choice. You can first online free download PassCollection's trial version of exercises and answers about Amazon Certification MLS-C01 Exam as a try, then you will be more confident to choose PassCollection's product to prepare for Amazon certification MLS-C01 exam. If you fail the exam, we will give you a full refund.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q177-Q182):

NEW QUESTION # 177
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 the NER deep learning model to extract entities.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: D

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 # 178
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. Cluster raw data using k-means and use sample data from each cluster to build a new dataset
  • B. Use an autoencoder or principal component analysis (PCA) to replace original features with new features
  • C. Normalize all numerical values to be between 0 and 1
  • D. Run self-correlation on all features and remove highly correlated features

Answer: B

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 # 179
A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

  • A. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
  • B. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single-agent reinforcement learning problem.
  • C. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem.
  • D. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.

Answer: C

Explanation:
The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. This is because:
Multi-agent reinforcement learning (MARL) is a subfield of reinforcement learning that deals with learning and coordination of multiple agents that interact with each other and the environment 1. MARL can be applied to problems that involve distributed decision making, such as traffic signal control, where each traffic light can be modeled as an agent that observes the traffic state and chooses an action (e.g., changing the signal phase) to optimize a reward function (e.g., minimizing the delay or congestion) 2.
A correlated equilibrium is a solution concept in game theory that generalizes the notion of Nash equilibrium. It is a probability distribution over the joint actions of the agents that satisfies the following condition: no agent can improve its expected payoff by deviating from the distribution, given that it knows the distribution and the actions of the other agents 3. A correlated equilibrium can capture the correlation among the agents' actions, which is useful for modeling the traffic behavior at each light that is subject to a small stochastic error term.
A correlated equilibrium policy is a policy that induces a correlated equilibrium in a MARL setting. It can be obtained by using various methods, such as policy gradient, actor-critic, or Q-learning algorithms, that can learn from the feedback of the environment and the communication among the agents 4. A correlated equilibrium policy can achieve a better performance than a Nash equilibrium policy, which assumes that the agents act independently and ignore the correlation among their actions 5.
Therefore, by obtaining a correlated equilibrium policy by formulating this problem as a MARL problem, the data scientist can most effectively model the traffic behavior and reduce congestion.
References:
Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning for Traffic Signal Control: A Survey Correlated Equilibrium Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments Correlated Q-Learning


NEW QUESTION # 180
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

  • A. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
  • B. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
  • C. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
  • D. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.

Answer: B

Explanation:
Explanation
Pipe input mode is a feature of Amazon SageMaker that allows streaming large datasets from Amazon S3 directly to the training algorithm without downloading them to the local disk. This reduces the startup time, disk space, and cost of training jobs. Pipe input mode is supported by most of the built-in algorithms and can also be used with custom training algorithms. To use Pipe input mode, the data needs to be in a binary format such as protobuf recordIO or TFRecord. The training code needs to use the PipeModeDataset class to read the data from the named pipe provided by SageMaker. To verify that the training code and the model parameters are working as expected, it is recommended to train locally on a smaller subset of the data before launching a full-scale training job on SageMaker. This approach is faster and more efficient than the other options, which involve either downloading the full dataset to an EC2 instance or using AWS Glue, which is not designed for training machine learning models. References:
Using Pipe input mode for Amazon SageMaker algorithms
Using Pipe Mode with Your Own Algorithms
PipeModeDataset Class


NEW QUESTION # 181
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • A. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • B. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  • C. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  • D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Answer: D


NEW QUESTION # 182
......

As a IT worker sometime you may know you will take advantage of new technology more quickly by farming out computer operations, we prefer to strengthen own strong points. Our MLS-C01 test braindump materials is popular based on that too. As we all know the passing rate for IT exams is low, the wise choice for candidates will select valid MLS-C01 test braindump materials to make you pass exam surely and fast. Professional handles professional affairs.

Exam Cram MLS-C01 Pdf: https://www.passcollection.com/MLS-C01_real-exams.html

2025 Latest PassCollection MLS-C01 PDF Dumps and MLS-C01 Exam Engine Free Share: https://drive.google.com/open?id=1A7bNMD4PNYB8xe6Mo6Ba6gZtxY6lx8u9

Report this page