Amazon AIF-C01 Latest Exam Pdf - AIF-C01 Exam Objectives
Amazon AIF-C01 Latest Exam Pdf - AIF-C01 Exam Objectives
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Tags: AIF-C01 Latest Exam Pdf, AIF-C01 Exam Objectives, AIF-C01 Testking Learning Materials, AIF-C01 Valid Exam Braindumps, AIF-C01 Reliable Study Plan
The web-based Amazon AIF-C01 practice test software is designed explicitly for the AWS Certified AI Practitioner exam. It is a well-known self-preparation tool that contains AIF-C01 Exam Questions approved by Amazon Certified Professionals. Our Amazon AIF-C01 exam questions are periodically updated and are similar to the real AWS Certified AI Practitioner exam questions. The Amazon AIF-C01 Practice Test has a close resemblance with the actual Amazon AIF-C01 exam. Multiple This Amazon certification exam needs to be finished in a certain time duration, therefore Amazon AIF-C01 practice test allows candidates to practice in the allocated time set according to their own needs.
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>> Amazon AIF-C01 Latest Exam Pdf <<
AIF-C01 Exam Objectives - AIF-C01 Testking Learning Materials
Our objective is to make Amazon AIF-C01 test preparation process of every aspirant smooth. Therefore, we have introduced three formats of our AWS Certified AI Practitioner AIF-C01 Exam Questions. To ensure the best quality of each format, we have tapped the services of experts. They thoroughly analyze AWS Certified AI Practitioner AIF-C01 Exam’s content, Amazon AIF-C01 past tests, and add the AIF-C01 real exam questions in our three formats.
Amazon AWS Certified AI Practitioner Sample Questions (Q43-Q48):
NEW QUESTION # 43
A company is building a contact center application and wants to gain insights from customer conversations. The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?
- A. Transcribe call recordings by using Amazon Transcribe.
- B. Extract information from call recordings by using Amazon SageMaker Model Monitor.
- C. Build a conversational chatbot by using Amazon Lex.
- D. Create classification labels by using Amazon Comprehend.
Answer: A
NEW QUESTION # 44
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)
- A. Amazon Bedrock
- B. Amazon Comprehend
- C. Amazon Polly
- D. Amazon Lex
- E. Amazon Rekognition
Answer: A,B
NEW QUESTION # 45
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?
- A. Increase the model training time.
- B. Increase the volume of data that is used in training.
- C. Reduce the volume of data that is used in training.
- D. Add hyperparameters to the model.
Answer: B
Explanation:
When a model performs well on the training data but poorly in production, it is often due to overfitting.
Overfitting occurs when a model learns patterns and noise specific to the training data, which does not generalize well to new, unseen data in production. Increasing the volume of data used in training can help mitigate this problem by providing a more diverse and representative dataset, which helps the model generalize better.
* Option C (Correct): "Increase the volume of data that is used in training": Increasing the data volume can help the model learn more generalized patterns rather than specific features of the training dataset, reducing overfitting and improving performance in production.
* Option A: "Reduce the volume of data that is used in training" is incorrect, as reducing data volume would likely worsen the overfitting problem.
* Option B: "Add hyperparameters to the model" is incorrect because adding hyperparameters alone does not address the issue of data diversity or model generalization.
* Option D: "Increase the model training time" is incorrect because simply increasing training time does not prevent overfitting; the model needs more diverse data.
AWS AI Practitioner References:
* Best Practices for Model Training on AWS: AWS recommends using a larger and more diverse training dataset to improve a model's generalization capability and reduce the risk of overfitting.
NEW QUESTION # 46
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. Confusion matrix
- B. Correlation matrix
- C. R2 score
- D. Mean squared error (MSE)
Answer: A
Explanation:
A confusion matrix is the correct metric for evaluating the performance of a classification model, such as the deep learning model built to classify types of materials in images.
* Confusion Matrix:
* It is a table used to describe the performance of a classification model by comparing the actual and predicted classifications.
* Provides detailed insights into the model's performance, including true positives, true negatives, false positives, and false negatives.
* Why Option A is Correct:
* Performance Measurement: Helps measure various performance metrics like accuracy, precision, recall, and F1-score, which are critical for evaluating a classification model.
* Comprehensive Evaluation: Allows for a thorough analysis of where the model is making errors and the types of errors being made.
* Why Other Options are Incorrect:
* B. Correlation matrix: Used to identify relationships between variables, not for evaluating classification performance.
* C. R2 score: Used for regression models, not classification.
* D. Mean squared error (MSE): Also a metric for regression, measuring the average of the squares of the errors.
NEW QUESTION # 47
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
- A. Purchase Provisioned Throughput for the custom model.
- B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
- C. Register the model with the Amazon SageMaker Model Registry.
- D. Grant access to the custom model in Amazon Bedrock.
Answer: B
Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.
NEW QUESTION # 48
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In the present situation, you will find companies laying off their employees without any notice or prior information. They are just receiving an email and the next moment they have no access to the company network. So to avoid all this, you have to keep yourself updated with the new version of technologies and applications. You have to become one of AWS Certified AI Practitioner (AIF-C01) certification holders who survived the laying off situation and are still in a great position in their company. You cannot afford to lose it when you need your job the most.
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