RELIABLE MLS-C01 TEST PRACTICE & POSITIVE MLS-C01 FEEDBACK

Reliable MLS-C01 Test Practice & Positive MLS-C01 Feedback

Reliable MLS-C01 Test Practice & Positive MLS-C01 Feedback

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Tags: Reliable MLS-C01 Test Practice, Positive MLS-C01 Feedback, MLS-C01 Valid Dumps Questions, Exam MLS-C01 Review, MLS-C01 Test Registration

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To take the Amazon MLS-C01 Exam, candidates must first pass the AWS Certified Cloud Practitioner or AWS Certified Solutions Architect Associate exam. MLS-C01 exam consists of 65 multiple-choice and multiple-response questions and must be completed in 180 minutes. MLS-C01 exam is available in several languages, including English, Japanese, Korean, and Simplified Chinese. Upon passing the exam, candidates will receive an AWS Certified Machine Learning – Specialty certification, which is valid for three years. AWS Certified Machine Learning - Specialty certification demonstrates to employers and clients that the individual has the skills and knowledge needed to design, implement, and maintain machine learning solutions on the AWS platform.

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Recommended Experience

Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Certification Exam is designed to assess the knowledge and skills of individuals in the field of machine learning. AWS Certified Machine Learning - Specialty certification is intended for professionals who have experience in building, training, and deploying machine learning models on the Amazon Web Services (AWS) platform. MLS-C01 Exam Tests the ability of candidates to design and implement machine learning solutions using AWS services.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q189-Q194):

NEW QUESTION # 189
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test dat a. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  • A. Lower the min_child_weight parameter value.
  • B. Lower the max_depth parameter value.
  • C. Update the objective to binary:logistic.
  • D. Increase the max_depth parameter value.

Answer: B

Explanation:
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters


NEW QUESTION # 190
The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

  • A. Convolutional neural network (CNN)
  • B. K-means
  • C. Recurrent neural network (RNN)
  • D. Latent Dirichlet Allocation (LDA)

Answer: A

Explanation:
Explanation
The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.
Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states.
RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.
References:
Image Recognition Software - ML Image & Video Analysis - Amazon ...
Image classification and object detection using Amazon Rekognition ...
AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...
GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...
Meet iNaturalist, an AWS-powered nature app that helps you identify ...


NEW QUESTION # 191
A machine learning (ML) specialist is administering a production Amazon SageMaker endpoint with model monitoring configured. Amazon SageMaker Model Monitor detects violations on the SageMaker endpoint, so the ML specialist retrains the model with the latest dataset. This dataset is statistically representative of the current production traffic. The ML specialist notices that even after deploying the new SageMaker model and running the first monitoring job, the SageMaker endpoint still has violations.
What should the ML specialist do to resolve the violations?

  • A. Manually trigger the monitoring job to re-evaluate the SageMaker endpoint traffic sample.
  • B. Delete the endpoint and recreate it with the original configuration.
  • C. Run the Model Monitor baseline job again on the new training set. Configure Model Monitor to use the new baseline.
  • D. Retrain the model again by using a combination of the original training set and the new training set.

Answer: C

Explanation:
The ML specialist should run the Model Monitor baseline job again on the new training set and configure Model Monitor to use the new baseline. This is because the baseline job computes the statistics and constraints for the data quality and model quality metrics, which are used to detect violations. If the training set changes, the baseline job should be updated accordingly to reflect the new distribution of the data and the model performance. Otherwise, the old baseline may not be representative of the current production traffic and may cause false alarms or miss violations. References:
Monitor data and model quality - Amazon SageMaker
Detecting and analyzing incorrect model predictions with Amazon SageMaker Model Monitor and Debugger | AWS Machine Learning Blog


NEW QUESTION # 192
A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.
Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.
Which architecture should be used to scale the solution at the lowest cost?

  • A. Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler
  • B. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
  • C. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler
  • D. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

Answer: A


NEW QUESTION # 193
A Machine Learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar- sized datasets. The team's leaders need to accelerate the training process.
What can a Machine Learning Specialist do to address this concern?

  • A. Use AWS Glue to transform the CSV dataset to the JSON format.
  • B. Use Amazon Machine Learning to train the models.
  • C. Use Amazon SageMaker Pipe mode.
  • D. Use Amazon Kinesis to stream the data to Amazon SageMaker.

Answer: C

Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure


NEW QUESTION # 194
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