Question # 1
| A company needs to train an ML model to classify images of different types of
animals. The company has a large dataset of labeled images and will not label more
data. Which type of learning should the company use to train the model? | | A. Supervised learning. | | B. Unsupervised learning. | | C. Reinforcement learning. | | D. Active learning. |
A. Supervised learning.
Explanation: Supervised learning is appropriate when the dataset is labeled. The model
uses this data to learn patterns and classify images. Unsupervised learning, reinforcement
learning, and active learning are not suitable since they either require unlabeled data or
different problem settings. References: AWS Machine Learning Best Practices.
Question # 2
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements? | | A. Configure the security and compliance by using Amazon Inspector. | | B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
| | C. Encrypt and secure training data by using Amazon Macie.
| | D. Gather more data. Use Amazon Rekognition to add custom labels to the data.
|
B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
Question # 3
A company wants to build an ML model by using Amazon SageMaker. The company needs
to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements? | | A. Amazon SageMaker Feature Store | | B. Amazon SageMaker Data Wrangler | | C. Amazon SageMaker Clarify | | D. Amazon SageMaker Model Cards |
A. Amazon SageMaker Feature Store
Question # 4
| Which feature of Amazon OpenSearch Service gives companies the ability to build vector
database applications? | | A. Integration with Amazon S3 for object storage | | B. Support for geospatial indexing and queries | | C. Scalable index management and nearest neighbor search capability | | D. Ability to perform real-time analysis on streaming data |
Explanation:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced
capabilities to support vector search, which allows companies to build vector database
applications. This is particularly useful in machine learning, where vector representations
(embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core
features enabling vector database functionalities in OpenSearch. The service allows users
to index high-dimensional vectors and perform efficient nearest neighbor searches, which
are crucial for tasks such as recommendation systems, anomaly detection, and semantic
search.
Here is why option C is the correct answer:
Scalable Index Management: OpenSearch Service supports scalable indexing of
vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost-effective and performance-optimized way.
The service leverages underlying AWS infrastructure to ensure that indexing
scales seamlessly with data size.
Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor
search capability allows for fast and efficient searches over vector data. This is
essential for applications like product recommendation engines, where the system
needs to quickly find the most similar items based on a user's query or behavior.
AWS AI Practitioner References:
The other options do not directly relate to building vector database applications:
A. Integration with Amazon S3 for object storage is about storing data objects, not
vector-based searching or indexing.
B. Support for geospatial indexing and queries is related to location-based data,
not vectors used in machine learning.
D. Ability to perform real-time analysis on streaming data relates to analyzing
incoming data streams, which is different from the vector search capabilities.
Question # 5
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.
Which solution meets these requirements? | | A. Use Amazon Macie to scan the model's output for sensitive data and set up alerts for potential violations. | | B. Configure AWS CloudTrail to monitor the model's responses and create alerts for any detected personal information. | | C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations. | | D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alerts when model quality degrades. |
C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations.
Question # 6
A company wants to display the total sales for its top-selling products across various retail
locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs? | | A. Amazon Q in Amazon EC2 | | B. Amazon Q Developer | | C. Amazon Q in Amazon QuickSight | | D. Amazon Q in AWS Chatbot |
C. Amazon Q in Amazon QuickSight
Question # 7
A retail store wants to predict the demand for a specific product for the next few weeks by
using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement? | | A. Text data | | B. Image data | | C. Time series data | | D. Binary data |
C. Time series data
Explanation:
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting
scalar (one-dimensional) time series data. Time series data consists of sequences of data
points indexed in time order, typically with consistent intervals between them. In the context
of a retail store aiming to predict product demand, relevant time series data might include
historical sales figures, inventory levels, or related metrics recorded over regular time
intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series
data, the store can generate forecasts for future product demand. This capability is particularly useful for inventory management, staffing, and supply chain optimization. Other
data types, such as text, image, or binary data, are not suitable for time series forecasting
tasks and would not be appropriate inputs for the DeepAR algorithm.
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