Practice Quiz
A company has a dataset containing sensor readings and needs to deploy a model that can find anomalous data points diverging from patterned data. Which built-in unsupervised Amazon SageMaker algorithm is designed for this task?
The SageMaker AI Random Cut Forest (RCF) algorithm is an unsupervised algorithm specifically designed to detect anomalous data points that diverge from patterned data. Other options like DeepAR and Object2Vec are supervised, and IP Insights is unsupervised but specifically designed for learning usage patterns of IPv4 addresses rather than general anomaly detection.
A financial analysis firm wants to implement a supervised learning model to forecast scalar (one-dimensional) time series data. Which built-in SageMaker algorithm should they select to perform this forecasting using recurrent neural networks (RNN)?
The SageMaker AI DeepAR forecasting algorithm is a supervised learning algorithm designed to forecast scalar (one-dimensional) time series using recurrent neural networks (RNN). LDA, RCF, and IP Insights are unsupervised algorithms and are not designed for forecasting scalar time series using RNNs.
Which unsupervised built-in Amazon SageMaker algorithm is used to determine topics within a set of documents without using labeled training data?
The Latent Dirichlet Allocation (LDA) algorithm is an unsupervised algorithm used in SageMaker AI to determine topics within a set of documents without using labeled training data. BlazingText is optimized for text classification and Word2vec, Object2Vec is for feature engineering, and DeepAR is for forecasting.
A security analyst wants to configure an Amazon SageMaker model that can learn the usage patterns for IPv4 addresses to capture associations between those addresses and entities like user IDs or account numbers. Which built-in algorithm is designed for this purpose?
SageMaker AI's IP Insights is an unsupervised learning algorithm that learns usage patterns for IPv4 addresses to capture associations between IPv4 addresses and entities such as user IDs or account numbers.
Which AWS capability provides pre-trained models, pre-built solution templates, and examples for popular problem types using the SageMaker SDK and Studio Classic?
Amazon SageMaker JumpStart offers pre-trained models, pre-built solution templates, and examples for popular problem types using the SageMaker SDK and Studio Classic. DeepAR, Object2Vec, and RCF are specific built-in algorithms rather than portals/capabilities offering templates and models.
A company is launching an interactive customer-facing application and needs to deploy a machine learning model. The workload is characterized by real-time, interactive, and low-latency requirements. Furthermore, the deployment must handle fluctuating traffic using an infrastructure that supports automatic scaling. Which option meets these needs?
Real-time inference in Amazon SageMaker AI is designed specifically for workloads with real-time, interactive, and low-latency requirements. Furthermore, deploying a model to SageMaker AI hosting services provides a fully managed endpoint that supports automatic scaling to handle varying inference traffic.
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An AI engineering team is planning to host a machine learning model for an interactive customer application. The workload demands interactive, low-latency, and real-time performance. Additionally, the team needs a fully managed hosting solution that supports automatic scaling and provides enhanced metrics to monitor individual instances and containers. Which AWS capability meets these requirements?
Amazon SageMaker AI Real-time inference is ideal for inference workloads that require interactive, real-time, and low latency performance. Furthermore, real-time endpoints for SageMaker AI hosting services are fully managed, support autoscaling, and allow monitoring of individual instances and containers using enhanced metrics, perfectly aligning with all requirements.
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Ready to test your recall?
A company has a dataset containing sensor readings and needs to deploy a model that can find anomalous data points diverging from patterned data. Which built-in unsupervised Amazon SageMaker algorithm is designed for this task?
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