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Amazon AWS Certified AI Practitioner Sample Questions (Q73-Q78):

NEW QUESTION # 73
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?

A. Training
B. Model deployment
C. Bias correction
D. Inference

Answer: D

NEW QUESTION # 74
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.
Which solution meets these requirements?

A. Create a fraud forecasting system.
B. Build a speech recognition system.
C. Develop an anomaly detection system.
D. Create a natural language processing (NLP) named entity recognition system.

Answer: C

Explanation:
An anomaly detection system is suitable for identifying unusual patterns or behaviors, such as suspicious IP addresses, which might indicate a potential threat.
* Anomaly Detection:
* Anomaly detection uses machine learning algorithms to identify deviations from normal behavior, such as unexpected traffic from a suspicious IP address.
* This is a common approach for identifying potential threats or malicious activity in cybersecurity applications.
* Why Option C is Correct:
* Detects Suspicious Behavior: An anomaly detection system can monitor and detect IP addresses that exhibit unusual or suspicious patterns.
* Real-time Monitoring: Provides continuous analysis of network traffic to identify potential security threats.
* Why Other Options are Incorrect:
* A. Speech recognition system: Is unrelated to detecting suspicious IP addresses.
* B. NLP named entity recognition: Focuses on identifying entities in text, not IP address analysis.
* D. Fraud forecasting system: Generally used for predicting fraud, not directly applicable to identifying suspicious IPs.
Thus, C is the correct answer for detecting suspicious IP addresses.

NEW QUESTION # 75
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?

A. Amazon Lex
B. AWS Glue
C. Amazon Kinesis Data Streams
D. Amazon Rekognition

Answer: B

Explanation:
AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.
* AWS Glue:
* A fully managed extract, transform, and load (ETL) service that makes it easy to prepare and transform unstructured data into a structured format.
* Provides a range of tools for cleaning, enriching, and cataloging data, making it ready for data scientists to use in ML models.
* Why Option D is Correct:
* Data Transformation: AWS Glue can handle large volumes of data and transform unstructured data into structured formats efficiently.
* Integrated ML Support: Glue integrates with other AWS services to support ML workflows.
* Why Other Options are Incorrect:
* A. Amazon Lex: Used for building chatbots, not for data transformation.
* B. Amazon Rekognition: Used for image and video analysis, not for data transformation.
* C. Amazon Kinesis Data Streams: Handles real-time data streaming, not suitable for batch transformation of large volumes of unstructured data.

NEW QUESTION # 76
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

A. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
B. Evaluate the models by using built-in prompt datasets.
C. Use public model leaderboards to identify the model.
D. Evaluate the models by using a human workforce and custom prompt datasets.

Answer: D

NEW QUESTION # 77
A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language.
Which solution will align the LLM response quality with the company's expectations?

A. Choose an LLM of a different size.
B. Adjust the prompt.
C. Increase the Top K value.
D. Increase the temperature.

Answer: B

Explanation:
Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.
* Adjust the Prompt:
* Modifying the prompt can guide the LLM to produce outputs that are shorter and tailored to the desired language.
* A well-crafted prompt can provide specific instructions to the model, such as "Answer in a short sentence in Spanish."
* Why Option A is Correct:
* Control Over Output: Adjusting the prompt allows for direct control over the style, length, and language of the LLM outputs.
* Flexibility: Prompt engineering is a flexible approach to refining the model's behavior without modifying the model itself.
* Why Other Options are Incorrect:
* B. Choose an LLM of a different size: The model size does not directly impact the response length or language.
* C. Increase the temperature: Increases randomness in responses but does not ensure brevity or specific language.
* D. Increase the Top K value: Affects diversity in model output but does not align directly with response length or language specificity.

NEW QUESTION # 78
......

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Free PDF Quiz 2025 AIF-C01: AWS Certified AI Practitioner–Valid Reliable Exam Simulations

100% Pass-Rate Amazon Reliable AIF-C01 Exam Simulations and Pass-Sure AIF-C01 Practice Exam

Amazon AWS Certified AI Practitioner Sample Questions (Q73-Q78):

Free PDF Amazon AIF-C01 AWS Certified AI Practitioner First-grade Reliable Exam Simulations

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