Machine Learning Specialty (Page 23)

An online reseller has a large, multi-column dataset with one column missing 30% of its data.A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?Read More →

A retail company intends to use machine learning to categorize new products.A labeled dataset of current products was provided to the Data Science team.The dataset includes 1,200 products.The labeled dataset has 15 features for each product such as title dimensions, weight, and price.Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.Which model should be used for categorizing new products using the provided dataset for training?Read More →

A web-based company wants to improve its conversion rate on its landing page.Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker.However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation 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 →

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 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 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 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 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 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 →