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Amazon AWS Certified AI Practitioner Sample Questions (Q176-Q181):
NEW QUESTION # 176
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?
- A. Watermark detection for images
- B. Retrieval Augmented Generation (RAG)
- C. Data augmentation for imbalanced classes
- D. Model monitoring for class distribution
Answer: C
Explanation:
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
* Data Augmentation for Imbalanced Classes:
* Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
* Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
* Why Option A is Correct:
* Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
* Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
* Why Other Options are Incorrect:
* B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
* C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
* D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.
NEW QUESTION # 177
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
- A. Use batch inferencing to process detailed responses.
- B. Define a higher number for the temperature parameter.
- C. Set a low limit on the number of tokens the FM can produce.
- D. Experiment and refine the prompt until the FM produces the desired responses.
Answer: D
NEW QUESTION # 178
Which component of Amazon Bedrock Studio can help secure the content that AI systems generate?
- A. Knowledge bases
- B. Access controls
- C. Function calling
- D. Guardrails
Answer: D
NEW QUESTION # 179
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 Artifact
- B. AWS Trusted Advisor
- C. AWS Data Exchange
- D. AWS Audit Manager
Answer: C
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 # 180
A company has multiple datasets that contain historical dat
a. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting
Answer:
Explanation:
NEW QUESTION # 181
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