100% AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM COVERAGE & EXAM AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY INTRODUCTION

100% AWS-Certified-Machine-Learning-Specialty Exam Coverage & Exam AWS-Certified-Machine-Learning-Specialty Introduction

100% AWS-Certified-Machine-Learning-Specialty Exam Coverage & Exam AWS-Certified-Machine-Learning-Specialty Introduction

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To be eligible for the Amazon MLS-C01 exam, individuals must have prior experience in machine learning and a strong understanding of AWS services such as Amazon SageMaker, Amazon Kinesis, and Amazon Redshift. AWS-Certified-Machine-Learning-Specialty Exam is designed for professionals who are looking to advance their career in machine learning and demonstrate their ability to design, implement, and deploy machine learning solutions using AWS services.

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The AWS Certified Machine Learning - Specialty Certification Exam is a valuable credential for professionals looking to advance their careers in the field of machine learning. It is recognized globally and demonstrates the candidate's expertise in designing and implementing machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification can help professionals stand out in a competitive job market and open up new career opportunities in the field of machine learning.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q241-Q246):

NEW QUESTION # 241
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?

  • A. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
  • B. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
  • C. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
  • D. Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

Answer: A

Explanation:
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. SageMaker provides a variety of tools and frameworks to support the entire machine learning workflow, from data preparation to model deployment.
One of the tools that SageMaker offers is the Amazon SageMaker Python SDK, which is a high-level library that simplifies the interaction with SageMaker APIs and services. The SageMaker Python SDK allows you to write code in Python and use popular frameworks such as TensorFlow, PyTorch, MXNet, and more. You can use the SageMaker Python SDK to create and manage SageMaker resources such as notebook instances, training jobs, endpoints, and feature store.
If you need to continue working on a TensorFlow project using SageMaker for training without Wi-Fi access, the best approach is to download the TensorFlow Docker container used in SageMaker from GitHub to your local environment, and use the SageMaker Python SDK to test the code. This way, you can ensure that your code is compatible with the SageMaker environment and avoid any potential issues when you upload your code to SageMaker and start the training job. You can also use the same code to deploy your model to a SageMaker endpoint when you have Wi-Fi access again.
To download the TensorFlow Docker container used in SageMaker, you can visit the SageMaker Docker GitHub repository and follow the instructions to build the image locally. You can also use the SageMaker Studio Image Build CLI to automate the process of building and pushing the Docker image to Amazon Elastic Container Registry (Amazon ECR). To use the SageMaker Python SDK to test the code, you can install the SDK on your local machine by following the installation guide. You can also refer to the TensorFlow documentation for more details on how to use the SageMaker Python SDK with TensorFlow.
References:
SageMaker Docker GitHub repository
SageMaker Studio Image Build CLI
SageMaker Python SDK installation guide
SageMaker Python SDK TensorFlow documentation


NEW QUESTION # 242
A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.
Which solution will meet these requirements?

  • A. Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.
  • B. Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.
  • C. Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.
  • D. Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

Answer: C

Explanation:
When an Amazon SageMaker notebook instance needs to access encrypted data in Amazon S3, the ML specialist must ensure that both Amazon S3 access permissions and AWS Key Management Service (KMS) decryption permissions are properly configured. The dataset in this scenario is stored with server-side encryption using an AWS KMS key (SSE-KMS), so the following steps are necessary:
* S3 Read Permissions: Attach an IAM role to the SageMaker notebook instance with permissions that allow the s3:GetObject action for the specific S3 bucket storing the data. This will allow the notebook instance to read data from Amazon S3.
* KMS Key Policy Permissions: Grant permissions in the KMS key policy to the IAM role assigned to the SageMaker notebook instance. This allows SageMaker to use the KMS key to decrypt data in the S3 bucket.
These steps ensure the SageMaker notebook instance can access the encrypted data stored in S3. The AWS documentation emphasizes that to access SSE-KMS encrypted data, the SageMaker notebook requires appropriate permissions in both the S3 bucket policy and the KMS key policy, making Option C the correct and secure approach.


NEW QUESTION # 243
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. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
  • B. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
  • C. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • D. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • E. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.

Answer: A,D

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 # 244
A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish.
What steps could be used to accomplish this task? (Choose two.)

  • A. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.
  • B. Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language.
    Proceed with the analysis.
  • C. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.
  • D. Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
  • E. Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.

Answer: A,C

Explanation:
To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:
* Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.
* Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.
The other options are not valid because:
* Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.
* Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to- sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag- of-words format.
* Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:
* Amazon Translate
* Amazon Comprehend
* Amazon SageMaker
* Amazon SageMaker Neural Topic Model (NTM) Algorithm
* Amazon SageMaker BlazingText
* Amazon SageMaker Seq2Seq
* Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm
* Amazon Lex


NEW QUESTION # 245
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

  • A. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
  • B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
  • C. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.
  • D. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.

Answer: A

Explanation:
The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job. References: AWS Glue, Amazon SageMaker


NEW QUESTION # 246
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