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Amazon AWS Certified AI Practitioner Sample Questions (Q62-Q67):

NEW QUESTION # 62
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?

A. Real-time inference
B. Batch transform
C. Asynchronous inference
D. Serverless inference

Answer: A

Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
* Option A (Correct): "Real-time inference": This is the correct answer because it supports low- latency requirements, which are essential for real-time applications where quick response times are needed.
* Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
* Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
* Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner References:
* Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 63
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

A. Integration with Amazon S3 for object storage
B. Ability to perform real-time analysis on streaming data
C. Scalable index management and nearest neighbor search capability
D. Support for geospatial indexing and queries

Answer: C

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:
* According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
* The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
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.

NEW QUESTION # 64
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.
Which solution will meet these requirements?

A. Use Provisioned Throughput.
B. Customize the model by using fine-tuning.
C. Decrease the number of tokens in the prompt.
D. Increase the number of tokens in the prompt.

Answer: C

Explanation:
Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
* Token Reduction Strategy:
* By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.
* Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.
* Why Option B is Correct:
* Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.
* Maintaining Performance: If the model is already performing well, a reduction in tokens should not significantly impact its performance.
* Why Other Options are Incorrect:
* A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already performing well.
* C. Increase the number of tokens: Would increase costs, not lower them.
* D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in databases.

NEW QUESTION # 65
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?

A. AWS Data Exchange
B. AWS Artifact
C. AWS Audit Manager
D. AWS Trusted Advisor

Answer: A

Explanation:
AWS Data Exchange is a service that allows companies to securely exchange data with third parties, such as independent software vendors (ISVs). AWS Data Exchange can be configured to provide notifications, including email notifications, when new datasets or compliance reports become available.
* Option D (Correct): "AWS Data Exchange": This is the correct answer because it enables the company to receive notifications, including email messages, when ISVs' compliance reports are available.
* Option A: "AWS Audit Manager" is incorrect because it focuses on assessing an organization's own compliance, not receiving third-party compliance reports.
* Option B: "AWS Artifact" is incorrect as it provides access to AWS's compliance reports, not ISVs'.
* Option C: "AWS Trusted Advisor" is incorrect as it offers optimization and best practices guidance, not compliance report notifications.
AWS AI Practitioner References:
* AWS Data Exchange Documentation: AWS explains how Data Exchange allows organizations to subscribe to third-party data and receive notifications when updates are available.

NEW QUESTION # 66
An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.
Which metric will help the AI practitioner evaluate the performance of the model?

A. Correlation matrix
B. Mean squared error (MSE)
C. R2 score
D. Confusion matrix

Answer: D

NEW QUESTION # 67
......

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Test Amazon AIF-C01 Dumps Demo, Valid Exam AIF-C01 Preparation

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Amazon AWS Certified AI Practitioner Sample Questions (Q62-Q67):

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