Reliable MLS-C01 Exam Guide, MLS-C01 Reliable Test Review

Almost every AWS Certified Machine Learning - Specialty (MLS-C01) test candidate nowadays is confused about the AWS Certified Machine Learning - Specialty (MLS-C01) study material. They don't know where to download updated MLS-C01 questions that can help them prepare quickly for the AWS Certified Machine Learning - Specialty (MLS-C01) test. Some rely on outdated AWS Certified Machine Learning - Specialty (MLS-C01) questions and suffer from the loss of money and time.

To prepare for the AWS Certified Machine Learning - Specialty certification exam, candidates can take advantage of various resources provided by AWS, including online courses, practice exams, and whitepapers. Candidates can also attend training sessions and workshops offered by AWS partners and take advantage of AWS support and consulting services.

>> Reliable MLS-C01 Exam Guide <<

Use Amazon MLS-C01 Questions - Best Strategy To Beat The Exam Stress

Our MLS-C01 exam simulation is a great tool to improve our competitiveness. After we use our study materials, we can get the Amazon certification faster. This certification gives us more opportunities. Compared with your colleagues around you, with the help of our MLS-C01 preparation questions, you will also be able to have more efficient work performance. Our MLS-C01 Study Materials can bring you so many benefits because they have the following features. I hope you can use a cup of coffee to learn about our MLS-C01 training engine. Perhaps this is the beginning of your change.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q88-Q93):

NEW QUESTION # 88
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. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
C. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
D. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
E. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.

Answer: B,E

Explanation:
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.
References:
XGBoost Parameters
XGBoost for Imbalanced Classification

NEW QUESTION # 89
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

A. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
B. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.

Answer: B

NEW QUESTION # 90
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 the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
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 Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
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: B

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.
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 # 91
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.

How should the data scientist split the dataset into a training and test set for this use case?

A. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
B. Randomly select 10% of the users. Split off all interaction data from these users for the test set.
C. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
D. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.

Answer: B

Explanation:
The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data .
References:
* Amazon SageMaker Developer Guide: Train and Test Datasets
* Amazon Personalize Developer Guide: Preparing and Importing Data

NEW QUESTION # 92
A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train.
The ML specialist wants to decrease the training time of the model.
Which approaches will meet this requirement7 (SELECT TWO )

A. Configure model auto scaling dynamically to adjust the number of instances automatically.
B. Use multiple training instances.
C. Use a pre-trained version of the model. Run incremental training.
D. Replace On-Demand Instances with Spot Instances
E. Replace CPU-based EC2 instances with GPU-based EC2 instances.

Answer: B,E

Explanation:
The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:
* C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.
* D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.
The other options are not effective or relevant, because they have the following drawbacks:
* A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.
* B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.
* E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.
1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What's AI) Bouchard | Towards Data Science
2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog
3: DeepAR Forecasting Algorithm - Amazon SageMaker
4: Distributed Training - Amazon SageMaker
5: Managed Spot Training - Amazon SageMaker
6: Automatic Scaling - Amazon SageMaker
7: How the DeepAR Algorithm Works - Amazon SageMaker

NEW QUESTION # 93
......

Solutions is one of the top platforms that has been helping AWS Certified Machine Learning - Specialty exam candidates for many years. Over this long time period countless candidates have passed their dream AWS Certified Machine Learning - Specialty (MLS-C01) certification exam. They all got help from Exams. Solutions MLS-C01 Practice Questions and easily passed their exam. The Amazon MLS-C01 exam questions are designed by experience and qualified MLS-C01 certification expert.

MLS-C01 Reliable Test Review: https://www.exam4docs.com/MLS-C01-study-questions.html