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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions:

1. A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

A) Increase the temperature parameter and the top_k parameter.
B) Decrease the temperature parameter. Increase the top_k parameter.
C) Decrease the temperature parameter and the top_k parameter.
D) Increase the temperature parameter. Decrease the top_k parameter.

2. A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

A) Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
B) Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
C) Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
D) Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.

3. An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?

A) Apache Parquet files
B) JSON files compressed with gzip
C) JSON objects in JSONL format
D) CSV files compressed with Snappy

4. A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

A) Implement padding to ensure that all color feature vectors have the same length.
B) Perform dimensionality reduction on the color categories.
C) Apply label encoding to the color categories. Automatically assign each color a unique integer.
D) One-hot encode the color categories to transform the color scheme feature into a binary matrix.

5. A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
Which solution will meet this requirement?

A) Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.
B) Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
C) Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
D) Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.

Solutions: