MLS-C01 EXAMS TORRENT | MLS-C01 EXAM TORRENT

MLS-C01 Exams Torrent | MLS-C01 Exam Torrent

MLS-C01 Exams Torrent | MLS-C01 Exam Torrent

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam that tests individuals on their knowledge and skills related to machine learning on the Amazon Web Services (AWS) platform. MLS-C01 exam is designed for individuals who wish to become certified professionals in the field of machine learning and want to showcase their expertise in designing, implementing, deploying, and maintaining machine learning solutions on AWS.

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Amazon AWS Certified Machine Learning Specialty Exam is a rigorous certification process that can help demonstrate a professional's expertise in designing, building, and deploying machine learning solutions on AWS. By earning this certification, candidates can position themselves as highly skilled professionals in the field of machine learning and set themselves apart from others in the field.

Earning the Amazon MLS-C01 Certification demonstrates a high level of expertise in machine learning and AWS, and can lead to career advancement opportunities in various industries. It also helps organizations identify skilled professionals who can help them leverage the power of machine learning to solve complex business problems.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q316-Q321):

NEW QUESTION # 316
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

  • A. Create a FindMatches machine learning transform in AWS Glue.
  • B. Create an AWS Glue crawler to infer duplicate accounts in the source data.
  • C. Search for duplicate accounts in the AWS Glue Data Catalog.
  • D. Execute the built-in FindDuplicates Amazon Athena query.

Answer: A

Explanation:
The best strategy to identify fraudulent accounts is to create a FindMatches machine learning transform in AWS Glue. The FindMatches transform enables you to identify duplicate or matching records in your dataset, even when the records do not have a common unique identifier and no fields match exactly. This can help you improve fraud detection by finding accounts that are associated with a previously known fraudulent user. You can teach the FindMatches transform your definition of a "duplicate" or a "match" through examples, and it will use machine learning to identify other potential duplicates or matches in your dataset. You can then use the FindMatches transform in your AWS Glue ETL jobs to cleanse your data.
Option A is incorrect because there is no built-in FindDuplicates Amazon Athena query. Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. However, Amazon Athena does not provide a predefined query to find duplicate records in a dataset. You would have to write your own SQL query to perform this task, which might not be as effective or accurate as using the FindMatches transform.
Option C is incorrect because creating an AWS Glue crawler to infer duplicate accounts in the source data is not a valid strategy. An AWS Glue crawler is a program that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the AWS Glue Data Catalog. A crawler does not perform any data cleansing or record matching tasks.
Option D is incorrect because searching for duplicate accounts in the AWS Glue Data Catalog is not a feasible strategy. The AWS Glue Data Catalog is a central repository to store structural and operational metadata for your data assets. The Data Catalog does not store the actual data, but rather the metadata that describes where the data is located, how it is formatted, and what it contains. Therefore, you cannot search for duplicate records in the Data Catalog.
References:
Record matching with AWS Lake Formation FindMatches - AWS Glue
Amazon Athena - Interactive SQL Queries for Data in Amazon S3
AWS Glue Crawlers - AWS Glue
AWS Glue Data Catalog - AWS Glue


NEW QUESTION # 317
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. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • B. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • C. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • 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: C,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 # 318
A company wants to forecast the daily price of newly launched products based on 3 years of data for older product prices, sales, and rebates. The time-series data has irregular timestamps and is missing some values.
Data scientist must build a dataset to replace the missing values. The data scientist needs a solution that resamptes the data daily and exports the data for further modeling.
Which solution will meet these requirements with the LEAST implementation effort?

  • A. Use Amazon SageMaker Studio Notebook with Pandas.
  • B. Use Amazon EMR Serveriess with PySpark.
  • C. Use AWS Glue DataBrew.
  • D. Use Amazon SageMaker Studio Data Wrangler.

Answer: D

Explanation:
Explanation
Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. Data Wrangler is integrated with Amazon SageMaker Studio, a web-based IDE for machine learning, which makes it easy to access and use the tool. Data Wrangler is a serverless and fully managed service, which means the data scientist does not need to provision, configure, or manage any infrastructure or clusters.
Option A is incorrect because Amazon EMR Serverless is a serverless option for running big data analytics applications using open-source frameworks, such as Apache Spark. However, using Amazon EMR Serverless would require the data scientist to write PySpark code to perform the data preparation tasks, such as resampling, imputation, and aggregation. This would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.
Option B is incorrect because AWS Glue DataBrew is another visual data preparation tool that can be used to clean and normalize data without writing code. However, DataBrew does not support time-series data as a data type, and does not provide built-in transformations for resampling, interpolation, or aggregation of time-series data. Therefore, using DataBrew would not meet the requirements of the use case.
Option D is incorrect because using Amazon SageMaker Studio Notebook with Pandas would also require the data scientist to write Python code to perform the data preparation tasks. Pandas is a popular Python library for data analysis and manipulation, which supports time-series data and provides various methods for resampling, interpolation, and aggregation. However, using Pandas would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.
References:
1: Amazon SageMaker Data Wrangler documentation
2: Amazon EMR Serverless documentation
3: AWS Glue DataBrew documentation
4: Pandas documentation


NEW QUESTION # 319
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?

  • A. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • B. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.
  • C. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • D. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.

Answer: A

Explanation:
Explanation
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre-trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
References:
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 320
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. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  • B. 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.
  • C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

Answer: A


NEW QUESTION # 321
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