Amazon AIF-C01 Übungsprüfungen
Zuletzt aktualisiert am 26.04.2025- Prüfungscode: AIF-C01
- Prüfungsname: AWS Certified AI Practitioner
- Zertifizierungsanbieter: Amazon
- Zuletzt aktualisiert am: 26.04.2025
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 . Inference
- C . Model deployment
- D . Bias correction
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
- A . Code for model training
- B . Partial dependence plots (PDPs)
- C . Sample data for training
- D . Model convergence tables
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?
- A . Use Amazon Inspector to monitor SageMaker Studio.
- B . Use Amazon Macie to monitor SageMaker Studio.
- C . Configure SageMaker to use a VPC with an S3 endpoint.
- D . Configure SageMaker to use S3 Glacier Deep Archive.
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
- A . Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
- B . Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
- C . Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
- D . Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?
- A . Configure AWS CloudTrail as the logs destination for the model.
- B . Enable invocation logging in Amazon Bedrock.
- C . Configure AWS Audit Manager as the logs destination for the model.
- D . Configure model invocation logging in Amazon EventBridge.
A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs.
Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?
- A . AWS Audit Manager
- B . AWS CloudTrail
- C . Amazon Fraud Detector
- D . AWS Trusted Advisor
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
- A . AWS PrivateLink
- B . Amazon Macie
- C . Amazon CloudFront
- D . Internet gateway
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 . Build a speech recognition system.
- B . Create a natural language processing (NLP) named entity recognition system.
- C . Develop an anomaly detection system.
- D . Create a fraud forecasting system.
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 . Reduce the volume of data that is used in training.
- B . Add hyperparameters to the model.
- C . Increase the volume of data that is used in training.
- D . Increase the model training time.
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
- A . Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
- B . Mask the confidential data in the inference responses by using dynamic data masking.
- C . Encrypt the confidential data in the inference responses by using Amazon SageMaker.
- D . Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).