AIF-C01 Practice Questions

A.  

Create an Amazon Bedrock custom service role for each team that has access to only the team ' s customer data.

B.  

Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.

C.  

Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.

D.  

Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team ' s customer folders.

A.  

Use the AI solution to make final hiring decisions without human review.

B.  

Train the AI solution exclusively on data from previous successful hires.

C.  

Test the AI solution to ensure that it does not discriminate against any protected groups.

D.  

Keep the AI decision-making process confidential to maintain a competitive advantage.

A.  

A model that groups customers based on their purchase history

B.  

A model that classifies images as dogs or cats

C.  

A model that predicts a house ' s price based on various features

D.  

A model that learns to play chess by using trial and error

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.

A.  

Toxicity

B.  

Hallucinations

C.  

Interpretability

D.  

Deterministic outputs

A.  

Encrypt each document with encryption keys.

B.  

Create embeddings from document chunks.

C.  

Label the document data with metadata.

D.  

Generate one-hot encoding for each document.

A.  

The model is underfit.

B.  

The model requires prompt engineering.

C.  

The model is biased.

D.  

The model is overfit.

A.  

Use supervised learning to create a regression model that will predict probability.

B.  

Use reinforcement learning to train a model to return the probability.

C.  

Use code that will calculate probability by using simple rules and computations.

D.  

Use unsupervised learning to create a model that will estimate probability density.

A.  

A stock prediction model decreases in accuracy after testing on new data.

B.  

A loan default risk model uses only credit scores to assess risk.

C.  

A sales prediction model uses only one month to forecast yearly revenue.

D.  

A student performance model uses only the number of advanced classes that a student has taken to assess performance.

A.  

Object detection

B.  

Anomaly detection

C.  

Named entity recognition

D.  

Inpainting