Machine Learning Specialty (Page 25)

A Mobile Network Operator is building an analytics platform to analyze and optimize a company’s operations using Amazon Athena and Amazon S3.The source systems send data in .CSV format in real time.The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3.Which solution takes the LEAST effort to implement?Read More →

A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model.The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model’s performance.The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.Which preprocessing step will meet these requirements?Read More →

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end- to-end solution in AWS using Amazon SageMaker.The historical training data is stored in Amazon RDS.Which approach should the Specialist use for training a model using that data?Read More →

A data science team is planning to build a natural language processing (NLP) application.The application’s text preprocessing stage will include part-of-speech tagging and key phase extraction.The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.Which solution can the team build MOST quickly to meet these requirements?Read More →

A data scientist is using Amazon Comprehend to perform sentiment analysis on a dataset of one million social media posts.Which approach will process the dataset in the LEAST time?Read More →

A bank wants to launch a low-rate credit promotion.The bank is located in a town that recently experienced economic hardship.Only some of the bank’s customers were affected by the crisis, so the bank’s credit team must identify which customers to target with the promotion.However, the credit team wants to make sure that loyal customers’ full credit history is considered when the decision is made.The bank’s data science team developed a model that classifies account transactions and understands credit eligibility.The data science team used the XGBoost algorithm to train the model.The team used 7 years of bank transaction historical data for training and hyperparameter tuning over the course of several days.The accuracy of the model is sufficient, but the credit team is struggling to explain accurately why the model denies credit to some customers.The credit team has almost no skill in data science.What should the data science team do to address this issue in the MOST operationally efficient manner?Read More →

An ecommerce company is automating the categorization of its products based on images.A data scientist has trained a computer vision model using the AmazonSageMaker image classification algorithm.The images for each product are classified according to specific product lines.The accuracy of the model is too low when categorizing new products.All of the product images have the same dimensions and are stored within an Amazon S3 bucket.The company wants to improve the model so it can be used for new products as soon as possible.Which steps would improve the accuracy of the solution? (Choose three.)Read More →

A Machine Learning Specialist is designing a system for improving sales for a company.The objective is to use the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users.What should the Specialist do to meet this objective?Read More →

A cybersecurity company is collecting on-premises server logs, mobile app logs, and IoT sensor data.The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis.Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances.The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.Which solution will meet these requirements MOST cost-effectively?Read More →

A machine learning (ML) engineer is using Amazon SageMaker automatic model tuning (AMT) to optimize a model’s hyperparameters.The ML engineer notices that the tuning jobs take a long time to run.The tuning jobs continue even when the jobs are not significantly improving against the objective metric.The ML engineer needs the training jobs to optimize the hyperparameters more quickly.How should the ML engineer configure the SageMaker AMT data types to meet these requirements?Read More →