Amazon AWS Certified Machine Learning - Specialty
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#181 (Accuracy: 100% / 4 votes)
A company is launching a new product and needs to build a mechanism to monitor comments about the company and its new product on social media. The company needs to be able to evaluate the sentiment expressed in social media posts, and visualize trends and configure alarms based on various thresholds.
The company needs to implement this solution quickly, and wants to minimize the infrastructure and data science resources needed to evaluate the messages.
The company already has a solution in place to collect posts and store them within an Amazon S3 bucket.
What services should the data science team use to deliver this solution?
  • A. Train a model in Amazon SageMaker by using the BlazingText algorithm to detect sentiment in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when posts are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table and in a custom Amazon CloudWatch metric. Use CloudWatch alarms to notify analysts of trends.
  • B. Train a model in Amazon SageMaker by using the semantic segmentation algorithm to model the semantic content in the corpus of social media posts. Expose an endpoint that can be called by AWS Lambda. Trigger a Lambda function when objects are added to the S3 bucket to invoke the endpoint and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
  • C. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the    message and record the sentiment in an Amazon DynamoDB table. Schedule a second Lambda function to query recently added records and send an Amazon Simple Notification Service (Amazon SNS) notification to notify analysts of trends.
  • D. Trigger an AWS Lambda function when social media posts are added to the S3 bucket. Call Amazon Comprehend for each post to capture the sentiment in the message and record the sentiment in a custom Amazon CloudWatch metric and in S3. Use CloudWatch alarms to notify analysts of trends.
#182 (Accuracy: 100% / 2 votes)
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the
Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?
  • A. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
  • B. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.
  • C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
  • D. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
#183 (Accuracy: 100% / 2 votes)
A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only.
How should the Machine Learning Specialist transform the dataset to minimize query runtime?
  • A. Convert the records to Apache Parquet format.
  • B. Convert the records to JSON format.
  • C. Convert the records to GZIP CSV format.
  • D. Convert the records to XML format.
#184 (Accuracy: 100% / 2 votes)
A Machine Learning Specialist is developing a daily ETL workflow containing multiple ETL jobs. The workflow consists of the following processes:
* Start the workflow as soon as data is uploaded to Amazon S3.
* When all the datasets are available in Amazon S3, start an ETL job to join the uploaded datasets with multiple terabyte-sized datasets already stored in Amazon
S3.
* Store the results of joining datasets in Amazon S3.
* If one of the jobs fails, send a notification to the Administrator.
Which configuration will meet these requirements?
  • A. Use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
  • B. Develop the ETL workflow using AWS Lambda to start an Amazon SageMaker notebook instance. Use a lifecycle configuration script to join the datasets and persist the results in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
  • C. Develop the ETL workflow using AWS Batch to trigger the start of ETL jobs when data is uploaded to Amazon S3. Use AWS Glue to join the datasets in Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
  • D. Use AWS Lambda to chain other Lambda functions to read and join the datasets in Amazon S3 as soon as the data is uploaded to Amazon S3. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.
#185 (Accuracy: 100% / 4 votes)
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)
  • A. The factorization machines (FM) algorithm
  • B. The Latent Dirichlet Allocation (LDA) algorithm
  • C. The principal component analysis (PCA) algorithm
  • D. The k-means algorithm
  • E. The Random Cut Forest (RCF) algorithm
#186 (Accuracy: 100% / 3 votes)
A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.
What should the Specialist do to optimize the data for training on SageMaker?
  • A. Use the SageMaker batch transform feature to transform the training data into a DataFrame.
  • B. Use AWS Glue to compress the data into the Apache Parquet format.
  • C. Transform the dataset into the RecordIO protobuf format.
  • D. Use the SageMaker hyperparameter optimization feature to automatically optimize the data.
#187 (Accuracy: 100% / 3 votes)
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
  • A. Logistic regression
  • B. Linear regression
  • C. K-means
  • D. Principal component analysis (PCA)
#188 (Accuracy: 100% / 5 votes)
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?
  • A. Gather more data using Amazon Mechanical Turk and then retrain
  • B. Train an anomaly detection model instead of an MLP
  • C. Train an XGBoost model instead of an MLP
  • D. Add class weights to the MLP's loss function and then retrain
#189 (Accuracy: 100% / 2 votes)
A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.
Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the
SageMakerVariantInvocationsPerInstance setting?
  • A. 10
  • B. 30
  • C. 600
  • D. 2,400
#190 (Accuracy: 96% / 4 votes)
A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.
Which solution will meet these requirements?
  • A. Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
  • B. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy- matching search on the two datasets, and cleanse the data accordingly.
  • C. Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
  • D. Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.