Title: Quiz Amazon - Newest MLA-C01 - AWS Certified Machine Learning Engineer - Associa [Print This Page] Author: robston175 Time: yesterday 11:54 Title: Quiz Amazon - Newest MLA-C01 - AWS Certified Machine Learning Engineer - Associa DOWNLOAD the newest BraindumpsPrep MLA-C01 PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=14HfmFd3a1vqdFQMz-ncL1Rp4vUlMyUtL
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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 Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
B. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
C. 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.
D. 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.
Answer: B
Explanation:
Amazon Transcribeis well-suited for converting audio data into text, including Spanish.
Amazon Translatecan efficiently translate Spanish text into English if needed.
Amazon Bedrock, with theJurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.
NEW QUESTION # 30
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?
A. Accuracy
B. Mean absolute error (MAE)
C. Area Under the ROC Curve (AUC)
D. F1 score
Answer: B
NEW QUESTION # 31
An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions.
When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?
A. Deploy the model to an Amazon SageMaker real-time endpoint.
B. Deploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
C. Package the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.
D. Deploy the model to an Amazon SageMaker Asynchronous Inference endpoint.
Answer: D
Explanation:
SageMaker Asynchronous Inference is designed for models with long processing times and large payloads. It can handle input data up to 1 GB and avoids holding open connections during long inference runs, reducing operational overhead compared to managing EC2 or ECS infrastructure.
This makes it the best fit for the genetic algorithm model that takes minutes and processes large requests.
NEW QUESTION # 32
A company is planning to use an Amazon SageMaker prebuilt algorithm to create a recommendation model. The algorithm must be able to make predictions on high-dimensional sparse data. Which SageMaker algorithm should the company choose for the recommendation model?
A. Sequence-to-Sequence (seq2seq)
B. K-nearest neighbors (k-NN)
C. Principal component analysis (PCA)
D. Factorization Machines
Answer: D
Explanation:
The Factorization Machines algorithm in SageMaker is specifically designed for recommendation systems and works well with high-dimensional sparse data such as user-item interactions. It efficiently models variable interactions and is the best choice for building a recommendation model in this scenario.
NEW QUESTION # 33
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)
A. Deploy models by using AWS Lambda functions.
B. Use PyTorch or TensorFlow with the distributed training option.
C. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
D. Use Amazon SageMaker Ground Truth for data labeling.
E. Use AWS Trainium instances for training.
Answer: C,E
Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.
NEW QUESTION # 34
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