Machine Learning Specialty (Page 8)

A manufacturer of car engines collects data from cars as they are being driven.The data collected includes timestamp, engine temperature, rotations per minute(RPM), and other sensor readings.The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance.The engine data is loaded into a data lake for training.Which is the MOST suitable predictive model that can be deployed into production?Read More →

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?Read More →

A financial services company is building a robust serverless data lake on Amazon S3.The data lake should be flexible and meet the following requirements:✑ Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.✑ Support event-driven ETL pipelines✑ Provide a quick and easy way to understand metadataWhich approach meets these requirements?Read More →

A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs.The data will be stored in Amazon S3.The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset.A portion of the schema is as follows:TransactionTimestamp (Timestamp)CardName (Varchar)CardNo (Varchar)The data engineer must provide the data so that any row with a CardNo value of NULL is removed.Also, the TransactionTimestamp column must be separated into a TransactionDate column and a TransactionTime column.Finally, the CardName column must be renamed to NameOnCard.The data will be extracted on a monthly basis and will be loaded into an S3 bucket.The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation.The solution also must be automated and must minimize the load on the Amazon Redshift cluster.Which solution meets these requirements?Read More →

A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world.The data is transmitted every 30 seconds in the form of records that contain 50 fields.Each record is up to 1 KB in size.The security vendor uses Amazon Kinesis Data Streams to ingest the data.The vendor requires hourly summaries of the records that Kinesis Data Streams ingests.The vendor will use Amazon Athena to query the records and to generate the summaries.The Athena queries will target 7 to 12 of the available data fields.Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?Read More →

A company wants to predict the sale prices of houses based on available historical sales data.The target variable in the company’s dataset is the sale price.The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code.The company wants to use multi-variable linear regression to predict house sale prices.Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model’s complexity?Read More →

A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis.This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?Read More →

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City.One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.Which prior probability distribution should the ML Specialist use for this variable?Read More →

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?Read More →

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?Read More →