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[General] MLA-C01 Related Certifications, MLA-C01 Exam Revision Plan

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【General】 MLA-C01 Related Certifications, MLA-C01 Exam Revision Plan

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q164-Q169):NEW QUESTION # 164
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?
  • A. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • B. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
  • C. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  • D. Run the primary node, core nodes, and task nodes on Spot Instances.
Answer: B

NEW QUESTION # 165
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?
  • A. Anomaly detection
  • B. Semantic segmentation
  • C. Linear regression
  • D. Logistic regression
Answer: D

NEW QUESTION # 166
A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team's leaders need to accelerate the training process.
What can a machine learning specialist do to address this concern?
  • A. Use Amazon Kinesis to stream the data to Amazon SageMaker.
  • B. Use Amazon Machine Learning to train the models.
  • C. Use AWS Glue to transform the CSV dataset to the JSON format.
  • D. Use Amazon SageMaker Pipe mode.
Answer: D
Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances.

NEW QUESTION # 167
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?
  • A. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
  • B. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
  • C. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
  • D. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
Answer: C
Explanation:
SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.

NEW QUESTION # 168
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training.
An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.
What should the ML engineer do to meet the encryption requirement?
  • A. Enable inter-container traffic encryption.
  • B. Enable network isolation.
  • C. Configure traffic encryption by using security groups.
  • D. Enable VPC flow logs.
Answer: A
Explanation:
In distributed training and processing jobs, multiple instances and containers communicate with each other over the network to exchange gradients, parameters, and intermediate results. Even when jobs run inside a private VPC, network traffic between instances is not automatically encrypted at the application layer.
AWS documentation for Amazon SageMaker specifies that inter-container traffic encryption is the supported mechanism for encrypting data in transit between containers that participate in distributed training or processing jobs. When enabled, SageMaker uses TLS to encrypt all communication between containers across instances, ensuring confidentiality and compliance with security requirements.
Option A (network isolation) prevents containers from making outbound network calls but does not encrypt traffic between distributed instances. Option B is incorrect because security groups control traffic access, not encryption. Option D (VPC flow logs) is a monitoring feature and does not provide encryption.
Therefore, enabling inter-container traffic encryption is the correct and AWS-recommended solution.

NEW QUESTION # 169
......
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