A cybersecurity company is collecting on-premises server logs, mobile app logs, and IoT sensor data. The company backs up the ingested data in an Amazon S3 bucket and sends the ingested data to Amazon OpenSearch Service for further analysis. Currently, the company has a custom ingestion pipeline that is running on Amazon EC2 instances. The company needs to implement a new serverless ingestion pipeline that can automatically scale to handle sudden changes in the data flow.
Which solution will meet these requirements MOST cost-effectively?
A. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Configure the data sources to send data to the delivery streams.
B. Create one Amazon Kinesis data stream. Create two Amazon Data Firehose delivery streams to send data to the S3 bucket and OpenSearch Service. Connect the delivery streams to the data stream. Configure the data sources to send data to the data stream.
C. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the raw data to the S3 bucket. Configure the data sources to send data to the delivery stream.
D. Create one Amazon Kinesis data stream. Create one Amazon Data Firehose delivery stream to send data to OpenSearch Service. Configure the delivery stream to back up the data to the S3 bucket. Connect the delivery stream to the data stream. Configure the data sources to send data to the data stream.
A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical data. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.
Which solution will meet these requirements with the LEAST development effort?
A. Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.
B. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.
C. Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.
D. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket. Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.
An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.
A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark. The data scientist wants AWS to be responsible for the operational maintenance of the container.
Which solution will meet these requirements?
A. Package the code as the training script for an Amazon SageMaker scikit-learn container.
B. Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).
C. Package the code into a custom-built container. Push the container to AWS Fargate.
D. Package the code by extending an Amazon SageMaker scikit-learn container.
A data scientist is implementing a deep learning neural network model for an object detection task on images. The data scientist wants to experiment with a large number of parallel hyperparameter tuning jobs to find hyperparameters that optimize compute time.
The data scientist must ensure that jobs that underperform are stopped. The data scientist must allocate computational resources to well-performing hyperparameter configurations. The data scientist is using the hyperparameter tuning job to tune the stochastic gradient descent (SGD) learning rate, momentum, epoch, and mini-batch size.
Which technique will meet these requirements with LEAST computational time?
A. Grid search
B. Random search
C. Bayesian optimization
D. Hyperband
A company maintains a 2 TB dataset that contains information about customer behaviors. The company stores the dataset in Amazon S3. The company stores a trained model container in Amazon Elastic Container Registry (Amazon ECR).
A machine learning (ML) specialist needs to score a batch model for the dataset to predict customer behavior. The ML specialist must select a scalable approach to score the model.
Which solution will meet these requirements MOST cost-effectively?
A. Score the model by using AWS Batch managed Amazon EC2 Reserved Instances. Create an Amazon EC2 instance store volume and mount it to the Reserved Instances.
B. Score the model by using AWS Batch managed Amazon EC2 Spot Instances. Create an Amazon FSx for Lustre volume and mount it to the Spot Instances.
C. Score the model by using an Amazon SageMaker notebook on Amazon EC2 Reserved Instances. Create an Amazon EBS volume and mount it to the Reserved Instances.
D. Score the model by using Amazon SageMaker notebook on Amazon EC2 Spot Instances. Create an Amazon Elastic File System (Amazon EFS) file system and mount it to the Spot Instances.
A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.
The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.
Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?
A. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
B. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
C. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
D. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
A company that operates oil platforms uses drones to photograph locations on oil platforms that are difficult for humans to access to search for corrosion.
Experienced engineers review the photos to determine the severity of corrosion. There can be several corroded areas in a single photo. The engineers determine whether the identified corrosion needs to be fixed immediately, scheduled for
future maintenance, or requires no action. The corrosion appears in an average of 0.1% of all photos.
A data science team needs to create a solution that automates the process of reviewing the photos and classifying the need for maintenance.
Which combination of steps will meet these requirements? (Choose three.)
A. Use an object detection algorithm to train a model to identify corrosion areas of a photo.
B. Use Amazon Rekognition with label detection on the photos.
C. Use a k-means clustering algorithm to train a model to classify the severity of corrosion in a photo.
D. Use an XGBoost algorithm to train a model to classify the severity of corrosion in a photo.
E. Perform image augmentation on photos that contain corrosion.
F. Perform image augmentation on photos that do not contain corrosion.
A global company receives and processes hundreds of documents daily. The documents are in printed .pdf format or .jpg format.
A machine learning (ML) specialist wants to build an automated document processing workflow to extract text from specific fields from the documents and to classify the documents. The ML specialist wants a solution that requires low
maintenance.
Which solution will meet these requirements with the LEAST operational effort?
A. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use a SageMaker text classification model to classify the document.
B. Use a PaddleOCR model in Amazon SageMaker to detect and extract the required text and fields. Use Amazon Comprehend to classify the document.
C. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Rekognition to classify the document.
D. Use Amazon Textract to detect and extract the required text and fields. Use Amazon Comprehend to classify the document.
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Choose two.)
A. Specificity
B. False positive rate
C. Accuracy
D. F1 score
E. True positive rate
A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.
Which solution will meet these requirements?
A. Amazon S3 with S3 Cross-Region Replication (CRR)
B. Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region
C. Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability
D. AWS Storage Gateway Volume Gateway
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