Amazon AWS Certified Machine Learning - Specialty
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#171 (Accuracy: 100% / 5 votes)
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. Create the training jobs as AWS Batch jobs that use Amazon EC2 Spot Instances in a managed compute environment.
  • B. Use Amazon EC2 Spot Instances to run the training jobs. Use a Spot Instance interruption notice to save a snapshot of the model to Amazon S3 before an instance is terminated.
  • C. Use AWS Lambda to run the training jobs. Save model weights to Amazon S3.
  • D. Use managed spot training in Amazon SageMaker. Launch the training jobs with checkpointing enabled.
#172 (Accuracy: 100% / 6 votes)
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?
  • A. Put the TFRecord data into an Amazon S3 bucket. Use AWS Glue or AWS Lambda to reformat the data to protobuf format and store the data in a second S3 bucket. Point the SageMaker training invocation to the second S3 bucket.
  • B. Rewrite the train.py script to add a section that converts TFRecord data to protobuf format. Point the SageMaker training invocation to the local path of the data. Ingest the protobuf data instead of the TFRecord data.
  • C. Use SageMaker script mode, and use train.py unchanged. Point the SageMaker training invocation to the local path of the data without reformatting the training data.
  • D. Use SageMaker script mode, and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the SageMaker training invocation to the S3 bucket without reformatting the training data.
#173 (Accuracy: 100% / 6 votes)
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?
  • A. Change the endpoint instance to an ml.t3 burstable instance with the same vCPU number as the ml.m5.xlarge instance has.
  • B. Attach an Amazon Elastic Inference ml.eia2.medium accelerator to the endpoint instance.
  • C. Enable Amazon SageMaker Autopilot to automatically tune performance of the model.
  • D. Change the endpoint instance to use a memory optimized ML instance.
#174 (Accuracy: 100% / 8 votes)
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?
  • A. Create an Amazon EMR cluster. Create external tables in the Apache Hive metastore, referencing the data that is stored in the S3 bucket. Explore the data from the Hive console.
  • B. Use AWS Glue to crawl the S3 bucket and create tables in the AWS Glue Data Catalog. Use Amazon Athena to explore the data.
  • C. Create an Amazon Redshift cluster. Use the COPY command to ingest the data from Amazon S3. Explore the data from the Amazon Redshift query editor GUI.
  • D. Create an Amazon Redshift cluster. Create external tables in an external schema, referencing the S3 bucket that contains the data. Explore the data from the Amazon Redshift query editor GUI.
#175 (Accuracy: 96% / 8 votes)
A newspaper publisher has a table of customer data that consists of several numerical and categorical features, such as age and education history, as well as subscription status. The company wants to build a targeted marketing model for predicting the subscription status based on the table data.

Which Amazon SageMaker built-in algorithm should be used to model the targeted marketing?
  • A. Random Cut Forest (RCF)
  • B. XGBoost
  • C. Neural Topic Model (NTM)
  • D. DeepAR forecasting
#176 (Accuracy: 96% / 7 votes)
A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.
Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product."
The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.
Which functionality of Amazon Comprehend should the team use to meet these requirements?
  • A. Custom classification with multi-class mode
  • B. Custom classification with multi-label mode
  • C. Custom entity recognition
  • D. Built-in models
#177 (Accuracy: 100% / 4 votes)
An energy company has wind turbines, weather stations, and solar panels that generate telemetry data. The company wants to perform predictive maintenance on these devices. The devices are in various locations and have unstable internet connectivity.
A team of data scientists is using the telemetry data to perform machine learning (ML) to conduct anomaly detection and predict maintenance before the devices start to deteriorate. The team needs a scalable, secure, high-velocity data ingestion mechanism. The team has decided to use Amazon S3 as the data storage location.
Which approach meets these requirements?
  • A. Ingest the data by using an HTTP API call to a web server that is hosted on Amazon EC2. Set up EC2 instances in an Auto Scaling configuration behind an Elastic Load Balancer to load the data into Amazon S3.
  • B. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to use Amazon Kinesis Data Firehose to send data to an Amazon Kinesis data stream that is configured to write to an S3 bucket.
  • C. Ingest the data over Message Queuing Telemetry Transport (MQTT) to AWS IoT Core. Set up a rule in AWS IoT Core to direct all MQTT data to an Amazon Kinesis Data Firehose delivery stream that is configured to write to an S3 bucket.
  • D. Ingest the data over Message Queuing Telemetry Transport (MQTT) to Amazon Kinesis data stream that is configured to write to an S3 bucket.
#178 (Accuracy: 94% / 9 votes)
A data scientist is reviewing customer comments about a company's products. The data scientist needs to present an initial exploratory analysis by using charts and a word cloud. The data scientist must use feature engineering techniques to prepare this analysis before starting a natural language processing (NLP) model.
Which combination of feature engineering techniques should the data scientist use to meet these requirements? (Choose two.)
  • A. Named entity recognition
  • B. Coreference
  • C. Stemming
  • D. Term frequency-inverse document frequency (TF-IDF)
  • E. Sentiment analysis
#179 (Accuracy: 100% / 5 votes)
A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.
  • B. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
  • C. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset. Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
  • D. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.
#180 (Accuracy: 96% / 6 votes)
A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?
  • A. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • B. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • C. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • D. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.