MLA-C01 Practice Questions
AWS Certified Machine Learning Engineer - Associate
Last Update 4 days ago
Total Questions : 241
Dive into our fully updated and stable MLA-C01 practice test platform, featuring all the latest AWS Certified Associate exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.
Our free AWS Certified Associate practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about MLA-C01. Use this test to pinpoint which areas you need to focus your study on.
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model ' s performance by using live data and without affecting production end users.
Which solution will meet these requirements?
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records per second.
The company needs a scalable AWS solution to identify anomalous data points with the LEAST operational overhead.
Which solution will meet these requirements?
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
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?
An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU.
The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized.
Which solution will meet this requirement?
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar
dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?
A travel company has trained hundreds of geographic data models to answer customer questions by using Amazon SageMaker AI. Each model uses its own inferencing endpoint, which has become an operational challenge for the company.
The company wants to consolidate the models ' inferencing endpoints to reduce operational overhead.
Which solution will meet these requirements?
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and distributed 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 VP
C.
What should the ML engineer do to meet the encryption requirement?
An ML engineer wants to re-train an XGBoost model at the end of each month. A data team prepares the training data. The training dataset is a few hundred megabytes in size. When the data is ready, the data team stores the data as a new file in an Amazon S3 bucket.
The ML engineer needs a solution to automate this pipeline. The solution must register the new model version in Amazon SageMaker Model Registry within 24 hours.
Which solution will meet these requirements?
