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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified Machine Learning Engineer – Associate (MLA-C01) |
| Exam Number: | MLA-C01 |
| Exam Format: | Multiple response, Multiple choice, Matching, Ordering |
| Certificate Validity Period: | 3 years |
| Real Exam Qty: | 65 scored questions + 15 unscored questions |
| Passing Score: | 720/1000 |
| Available Languages: | English, Korean, Simplified Chinese, Japanese |
| Related Certifications: | AWS Certified Solutions Architect – Associate AWS Certified Data Engineer – Associate AWS Certified DevOps Engineer – Professional AWS Certified AI Practitioner |
| Exam Duration: | 130 minutes |
| Exam Price: | USD 150 |
| Recommended Training: | AWS Skill Builder - ML Engineer Associate Exam Prep Amazon SageMaker Documentation |
| Exam Registration: | AWS Certification Registration |
| Sample Questions: | Amazon MLA-C01 Sample Questions |
| Exam Way: | Online proctored or test center exam |
| Pre Condition: | Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/ |
Die Schulungsunterlagen zur Amazon MLA-C01 Zertifizierungsprüfung bestehen aus Testfragen sowie Antworten, die von den erfahrenen IT-Experten aus Zertpruefung durch ihre Praxis und Erforschungen entworfen werden. Die Schulungsunterlagen zur Amazon MLA-C01 Zertifizierungsprüfung sind zur Zeit die genaueste auf dem Markt. Sie können die Demo auf der Webseite Zertpruefung.de herunterladen. Sie werden Ihr Helfer sein, während Sie sich auf die Amazon MLA-C01 Zertifizierungsprüfung vorbereiten.
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122. Frage
A construction company is using Amazon SageMaker AI to train specialized custom object detection models to identify road damage. The company uses images from multiple cameras. The images are stored as JPEG objects in an Amazon S3 bucket.
The images need to be pre-processed by using computationally intensive computer vision techniques before the images can be used in the training job. The company needs to optimize data loading and pre-processing in the training job. The solution cannot affect model performance or increase compute or storage resources.
Which solution will meet these requirements?
Antwort: A
Begründung:
AWS documentation recommends using RecordIO format with lazy loading to optimize data input pipelines for image-based training workloads. RecordIO is a binary data format that enables sequential reads, reducing I
/O overhead and improving throughput during training.
By converting JPEG images into RecordIO format, the training job can read data more efficiently from Amazon S3. Lazy loading ensures that only the required data is loaded into memory when needed, which optimizes CPU utilization during computationally intensive preprocessing steps.
Option A (file mode) results in many small S3 GET requests, which can become a bottleneck for large image datasets. Option B changes training behavior and can negatively affect convergence and performance. Option C reduces image quality, which directly impacts model accuracy and violates the requirement.
AWS SageMaker documentation explicitly highlights RecordIO and lazy loading as best practices for high- performance image training pipelines, especially when preprocessing is CPU-intensive.
Therefore, Option D is the correct and AWS-aligned solution.
123. Frage
An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset.
The ML engineer needs to make changes before running the hyperparameter grid search again.
Which changes will improve the model's performance? (Select TWO.)
Antwort: B,D
Begründung:
The scenario describes a classic overfitting problem: the XGBoost model performs well on the training dataset but poorly on the test hold-out dataset. According to AWS Machine Learning and XGBoost documentation, overfitting occurs when a model is too complex and learns noise and patterns specific to the training data rather than generalizable relationships.
One effective way to address overfitting is to reduce model complexity. Option B, reducing the number of features, simplifies the hypothesis space and lowers the risk of fitting spurious correlations. Feature reduction is a recommended best practice when the model shows a large generalization gap between training and test error.
Another effective method is to increase regularization. Option D, increasing the L2 regularization parameter (lambda in XGBoost), penalizes large weights and discourages overly complex trees. AWS documentation explicitly notes that L2 regularization helps improve generalization by smoothing model parameters and reducing variance.
Option A would worsen overfitting by increasing complexity. Option C is incorrect because reducing the number of training samples generally increases overfitting risk. Option E would decrease regularization strength and further degrade test performance.
Therefore, reducing feature complexity and increasing L2 regularization are the correct changes.
124. Frage
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)
Antwort: A,D
Begründung:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.
125. Frage
A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance.
Which solution will allow the company to achieve its goals?
Antwort: C
Begründung:
AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure.
126. Frage
A gaming company needs to deploy a natural language processing (NLP) model to moderate a chat forum in a game. The workload experiences heavy usage during evenings and weekends but minimal activity during other hours.
Which solution will meet these requirements MOST cost-effectively?
Antwort: D
Begründung:
The key requirements in this scenario are variable traffic patterns and cost efficiency. The workload has unpredictable spikes during evenings and weekends, followed by long periods of low or no usage. According to AWS Machine Learning documentation, Amazon SageMaker Serverless Inference is specifically designed for such use cases.
SageMaker Serverless Inference automatically provisions, scales, and shuts down compute resources based on incoming inference requests. Customers are billed only for the compute time used during inference, not for idle resources. This makes it highly cost-effective for workloads with intermittent or spiky traffic, such as real- time chat moderation in gaming environments.
Option A is incorrect because batch transform jobs are intended for offline, large-scale inference and require fixed capacity during job execution. They are not suitable for real-time NLP moderation.
Option C is also incorrect because reserving an EC2 GPU instance incurs continuous costs regardless of utilization. This would be inefficient given the long idle periods described in the scenario.
Option D, SageMaker Asynchronous Inference, is designed for workloads with long processing times or large payloads and still requires endpoint provisioning. While it can handle traffic spikes, it does not scale down to zero in the same cost-efficient manner as Serverless Inference.
Therefore, Amazon SageMaker Serverless Inference is the most cost-effective and operationally efficient solution for deploying an NLP moderation model with highly variable usage patterns.
127. Frage
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