A company operates an amusement park. The company wants to collect, monitor, and store real-time traffic data at several park entrances by using strategically placed cameras. The company's security team must be able to immediately access the data for viewing. Stored data must be indexed and must be accessible to the company's data science team.
Which solution will meet these requirements MOST cost-effectively?
A. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in integration with Amazon Rekognition for viewing by the security team.
B. Use Amazon Kinesis Video Streams to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.
C. Use Amazon Rekognition Video and the GStreamer plugin to ingest the data for viewing by the security team. Use Amazon Kinesis Data Streams to index and store the data.
D. Use Amazon Kinesis Data Firehose to ingest, index, and store the data. Use the built-in HTTP live streaming (HLS) capability for viewing by the security team.
A company is training machine learning (ML) models on Amazon SageMaker by using 200 TB of data that is stored in Amazon S3 buckets. The training data consists of individual files that are each larger than 200 MB in size. The company needs a data access solution that offers the shortest processing time and the least amount of setup.
Which solution will meet these requirements?
A. Use File mode in SageMaker to copy the dataset from the S3 buckets to the ML instance storage.
B. Create an Amazon FSx for Lustre file system. Link the file system to the S3 buckets.
C. Create an Amazon Elastic File System (Amazon EFS) file system. Mount the file system to the training instances.
D. Use FastFile mode in SageMaker to stream the files on demand from the S3 buckets.
An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.
Which data transformation step should the data scientist take to improve the predictions of the model?
A. One-hot encoding
B. Cartesian product transformation
C. Quantile binning
D. Normalization
An online advertising company is developing a linear model to predict the bid price of advertisements in real time with low-latency predictions. A data scientist has trained the linear model by using many features, but the model is overfitting the training dataset. The data scientist needs to prevent overfitting and must reduce the number of features.
Which solution will meet these requirements?
A. Retrain the model with L1 regularization applied.
B. Retrain the model with L2 regularization applied.
C. Retrain the model with dropout regularization applied.
D. Retrain the model by using more data.
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results:
1.
True positive rate (TPR): 0.700
2.
False negative rate (FNR): 0.300
3.
True negative rate (TNR): 0.977
4.
False positive rate (FPR): 0.023
5.
Overall accuracy: 0.949
Which solution should the data scientist use to improve the performance of the model?
A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class in the training dataset. Retrain the model with the updated training data.
B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data.
C. Undersample the minority class.
D. Oversample the majority class.
A machine learning (ML) specialist is developing a deep learning sentiment analysis model that is based on data from movie reviews. After the ML specialist trains the model and reviews the model results on the validation set, the ML specialist discovers that the model is overfitting.
Which solutions will MOST improve the model generalization and reduce overfitting? (Choose three.)
A. Shuffle the dataset with a different seed.
B. Decrease the learning rate.
C. Increase the number of layers in the network.
D. Add L1 regularization and L2 regularization.
E. Add dropout.
F. Decrease the number of layers in the network.
An analytics company has an Amazon SageMaker hosted endpoint for an image classification model. The model is a custom-built convolutional neural network (CNN) and uses the PyTorch deep learning framework. The company wants to increase throughput and decrease latency for customers that use the model.
Which solution will meet these requirements MOST cost-effectively?
A. Use Amazon Elastic Inference on the SageMaker hosted endpoint.
B. Retrain the CNN with more layers and a larger dataset.
C. Retrain the CNN with more layers and a smaller dataset.
D. Choose a SageMaker instance type that has multiple GPUs.
An ecommerce company is collecting structured data and unstructured data from its website, mobile apps, and IoT devices. The data is stored in several databases and Amazon S3 buckets. The company is implementing a scalable repository to store structured data and unstructured data. The company must implement a solution that provides a central data catalog, self-service access to the data, and granular data access policies and encryption to protect the data.
Which combination of actions will meet these requirements with the LEAST amount of setup? (Choose three.)
A. Identify the existing data in the databases and S3 buckets. Link the data to AWS Lake Formation.
B. Identify the existing data in the databases and S3 buckets. Link the data to AWS Glue.
C. Run AWS Glue crawlers on the linked data sources to create a central data catalog.
D. Apply granular access policies by using AWS Identity and Access Management (1AM). Configure server-side encryption on each data source.
E. Apply granular access policies and encryption by using AWS Lake Formation.
F. Apply granular access policies and encryption by using AWS Glue.
An online retail company wants to develop a natural language processing (NLP) model to improve customer service. A machine learning (ML) specialist is setting up distributed training of a Bidirectional Encoder Representations from
Transformers (BERT) model on Amazon SageMaker. SageMaker will use eight compute instances for the distributed training.
The ML specialist wants to ensure the security of the data during the distributed training. The data is stored in an Amazon S3 bucket.
Which combination of steps should the ML specialist take to protect the data during the distributed training? (Choose three.)
A. Run distributed training jobs in a private VPC. Enable inter-container traffic encryption.
B. Run distributed training jobs across multiple VPCs. Enable VPC peering.
C. Create an S3 VPC endpoint. Then configure network routes, endpoint policies, and S3 bucket policies.
D. Grant read-only access to SageMaker resources by using an IAM role.
E. Create a NAT gateway. Assign an Elastic IP address for the NAT gateway.
F. Configure an inbound rule to allow traffic from a security group that is associated with the training instances.
A bank wants to launch a low-rate credit promotion campaign. The bank must identify which customers to target with the promotion and wants to make sure that each customer's full credit history is considered when an approval or denial decision is made.
The bank's data science team used the XGBoost algorithm to train a classification model based on account transaction features. The data science team deployed the model by using the Amazon SageMaker model hosting service. The accuracy of the model is sufficient, but the data science team wants to be able to explain why the model denies the promotion to some customers.
What should the data science team do to meet this requirement in the MOST operationally efficient manner?
A. Create a SageMaker notebook instance. Upload the model artifact to the notebook. Use the plot_importance() method in the Python XGBoost interface to create a feature importance chart for the individual predictions.
B. Retrain the model by using SageMaker Debugger. Configure Debugger to calculate and collect Shapley values. Create a chart that shows features and SHapley. Additive explanations (SHAP) values to explain how the features affect the model outcomes.
C. Set up and run an explainability job powered by SageMaker Clarify to analyze the individual customer data, using the training data as a baseline. Create a chart that shows features and SHapley Additive explanations (SHAP) values to explain how the features affect the model outcomes.
D. Use SageMaker Model Monitor to create Shapley values that help explain model behavior. Store the Shapley values in Amazon S3. Create a chart that shows features and SHapley Additive explanations (SHAP) values to explain how the features affect the model outcomes.
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