MLS-C01 — questions

An automotive company is using computer vision in its autonomous cars. The company has trained its models successfully by using transfer learning from a convolutional neural network (CNN). The models are trained with PyTorch through the use of the Amazon SageMaker SDK. The company wants to reduce the time that is required for performing inferences, given the low latency that is required for self-driving. Which solution should the company use to evaluate and improve the performance of the models?

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?

An ecommerce company wants to train a large image classification model with 10,000 classes. The company runs multiple model training iterations and needs to minimize operational overhead and cost. The company also needs to avoid loss of work and model retraining. Which solution will meet these requirements?

A retail company uses a machine learning (ML) model for daily sales forecasting. The model has provided inaccurate results for the past 3 weeks. At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company's ML team determines that the inaccuracies are occurring because of a change in the value distributions of the model features. The ML team must implement a solution that will detect when this type of change occurs in the future. Which solution will meet these requirements with the LEAST amount of operational overhead?

A machine learning (ML) specialist has prepared and used a custom container image with Amazon SageMaker to train an image classification model. The ML specialist is performing hyperparameter optimization (HPO) with this custom container image to produce a higher quality image classifier. The ML specialist needs to determine whether HPO with the SageMaker built-in image classification algorithm will produce a better model than the model produced by HPO with the custom container image. All ML experiments and HPO jobs must be invoked from scripts inside SageMaker Studio notebooks. How can the ML specialist meet these requirements in the LEAST amount of time?

A company wants to deliver digital car management services to its customers. The company plans to analyze data to predict the likelihood of users changing cars. The company has 10 TB of data that is stored in an Amazon Redshift cluster. The company's data engineering team is using Amazon SageMaker Studio for data analysis and model development. Only a subset of the data is relevant for developing the machine learning models. The data engineering team needs a secure and cost-effective way to export the data to a data repository in Amazon S3 for model development. Which solutions will meet these requirements? (Choose two.)

A company is building an application that can predict spam email messages based on email text. The company can generate a few thousand human-labeled datasets that contain a list of email messages and a label of "spam" or "not spam" for each email message. A machine learning (ML) specialist wants to use transfer learning with a Bidirectional Encoder Representations from Transformers (BERT) model that is trained on English Wikipedia text data. What should the ML specialist do to initialize the model to fine-tune the model with the custom data?

A company is using a legacy telephony platform and has several years remaining on its contract. The company wants to move to AWS and wants to implement the following machine learning features: • Call transcription in multiple languages • Categorization of calls based on the transcript • Detection of the main customer issues in the calls • Customer sentiment analysis for each line of the transcript, with positive or negative indication and scoring of that sentiment Which AWS solution will meet these requirements with the LEAST amount of custom model training?

A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset. How should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased. Which solution will meet these requirements with the LEAST development effort?

A bank wants to use a machine learning (ML) model to predict if users will default on credit card payments. The training data consists of 30,000 labeled records and is evenly balanced between two categories. For the model, an ML specialist selects the Amazon SageMaker built-in XGBoost algorithm and configures a SageMaker automatic hyperparameter optimization job with the Bayesian method. The ML specialist uses the validation accuracy as the objective metric. When the bank implements the solution with this model, the prediction accuracy is 75%. The bank has given the ML specialist 1 day to improve the model in production. Which approach is the FASTEST way to improve the model's accuracy?

A data scientist has 20 TB of data in CSV format in an Amazon S3 bucket. The data scientist needs to convert the data to Apache Parquet format. How can the data scientist convert the file format with the LEAST amount of effort?

A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices. The company's data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability. The company can tolerate up to 5 minutes of latency for data ingestion. The company needs a solution that can apply basic data transformation during the ingestion process. Which solution will meet these requirements with the MOST operational efficiency?

A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model. Which change will create the required transformed records with the LEAST operational overhead?

A sports broadcasting company is planning to introduce subtitles in multiple languages for a live broadcast. The commentary is in English. The company needs the transcriptions to appear on screen in French or Spanish, depending on the broadcasting country. The transcriptions must be able to capture domain-specific terminology, names, and locations based on the commentary context. The company needs a solution that can support options to provide tuning data. Which combination of AWS services and features will meet these requirements with the LEAST operational overhead? (Choose two.)

A data scientist at a retail company is forecasting sales for a product over the next 3 months. After preliminary analysis, the data scientist identifies that sales are seasonal and that holidays affect sales. The data scientist also determines that sales of the product are correlated with sales of other products in the same category. The data scientist needs to train a sales forecasting model that incorporates this information. Which solution will meet this requirement with the LEAST development effort?

A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10,000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types. How should the company prepare the data for the model to improve the model's accuracy?

A company stores its documents in Amazon S3 with no predefined product categories. A data scientist needs to build a machine learning model to categorize the documents for all the company's products. Which solution will meet these requirements with the MOST operational efficiency?

A sports analytics company is providing services at a marathon. Each runner in the marathon will have their race ID printed as text on the front of their shirt. The company needs to extract race IDs from images of the runners. Which solution will meet these requirements with the LEAST operational overhead?

A manufacturing company wants to monitor its devices for anomalous behavior. A data scientist has trained an Amazon SageMaker scikit-learn model that classifies a device as normal or anomalous based on its 4-day telemetry. The 4-day telemetry of each device is collected in a separate file and is placed in an Amazon S3 bucket once every hour. The total time to run the model across the telemetry for all devices is 5 minutes. What is the MOST cost-effective solution for the company to use to run the model across the telemetry for all the devices?