MLS-C01 (Page 6)

A company’s machine learning (ML) specialist is designing a scalable data storage solution for Amazon SageMaker.The company has an existing TensorFlow-based model that uses a train.py script.The model relies on static training data that is currently stored in TFRecord format.What should the ML specialist do to provide the training data to SageMaker with the LEAST development overhead?Read More →

A company is building a machine learning (ML) model to classify images of plants.An ML specialist has trained the model using the Amazon SageMaker built-in Image Classification algorithm.The model is hosted using a SageMaker endpoint on an ml.m5.xlarge instance for real-time inference.When used by researchers in the field, the inference has greater latency than is acceptable.The latency gets worse when multiple researchers perform inference at the same time on their devices.Using Amazon CloudWatch metrics, the ML specialist notices that the ModelLatency metric shows a high value and is responsible for most of the response latency.The ML specialist needs to fix the performance issue so that researchers can experience less latency when performing inference from their devices.Which action should the ML specialist take to meet this requirement?Read More →

A data scientist is working on a model to predict a company’s required inventory stock levels.All historical data is stored in .csv files in the company’s data lake on Amazon S3.The dataset consists of approximately 500 GB of data The data scientist wants to use SQL to explore the data before training the model.The company wants to minimize costs.Which option meets these requirements with the LEAST operational overhead?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 →

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 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 →

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 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 →

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 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 →