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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 Duration: | 130 minutes |
| Passing Score: | 720/1000 |
| Real Exam Qty: | 65 scored questions + 15 unscored questions |
| Exam Format: | Matching, Multiple choice, Ordering, Multiple response |
| Exam Price: | USD 150 |
| Related Certifications: | AWS Certified Data Engineer – Associate AWS Certified AI Practitioner AWS Certified DevOps Engineer – Professional AWS Certified Solutions Architect – Associate |
| Available Languages: | Korean, Simplified Chinese, English, Japanese |
| Certificate Validity Period: | 3 years |
| Recommended Training: | Amazon SageMaker Documentation AWS Skill Builder - ML Engineer Associate Exam Prep |
| 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/ |
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NEW QUESTION # 31
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?
Answer: A
Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.
NEW QUESTION # 32
A company is building an enterprise AI platform. The company must catalog models for production, manage model versions, and associate metadata such as training metrics with models. The company needs to eliminate the burden of managing different versions of models.
Which solution will meet these requirements?
Answer: C
Explanation:
The correct answer is B. Use the Amazon SageMaker Model Registry to catalog the models. Create model groups for each model to manage the model versions and to maintain associated metadata.
The Amazon SageMaker Model Registry is a managed repository within SageMaker designed specifically for production-grade ML model lifecycle management. It allows organizations to catalog models, track multiple versions of a model, associate rich metadata, and manage deployment workflows in a scalable, controlled manner. Each model can belong to a model group, which acts as a container for all versions of that particular model. Versions can store training metrics, hyperparameters, model artifacts, and other key metadata, enabling reproducibility, auditing, and automated promotion between stages (e.g., Staging # Production).
Option A, while using the Model Registry, relies on manually tagging versions and creating key-value pairs to store metadata. This approach is error-prone, lacks structured versioning, and does not integrate with SageMaker's deployment pipelines.
Options C and D suggest using Amazon ECR repositories. While ECR can store containerized model artifacts, it is not designed for ML-specific metadata, versioning, or automated model stage transitions. Using ECR alone would require custom-built solutions for metadata management, auditing, and version tracking, adding unnecessary operational overhead.
By leveraging the Model Registry with model groups, organizations can automate promotions, apply approval workflows, and track lineage efficiently, fully aligning with AWS best practices for ML model development and production readiness. This ensures compliance, reproducibility, and reduces operational complexity in enterprise AI platforms.
Using the Model Registry and model groups is the standard AWS-recommended approach for enterprise-scale model cataloging and version control, enabling teams to focus on model improvement rather than infrastructure management.
NEW QUESTION # 33
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?
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 # 34
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 35
An ML engineer is developing a neural network to run on new user data. The dataset has dozens of floating- point features. The dataset is stored as CSV objects in an Amazon S3 bucket. Most objects and columns are missing at least one value. All features are relatively uniform except for a small number of extreme outliers.
The ML engineer wants to use Amazon SageMaker Data Wrangler to handle missing values before passing the dataset to the neural network.
Which solution will provide the MOST complete data?
Answer: A
Explanation:
The primary goal is to produce the most complete dataset while handling missing values and extreme outliers appropriately. Dropping samples (Option A) or columns (Option D) would reduce data completeness and potentially remove valuable information, which contradicts the requirement.
Imputation is therefore the correct approach. Between mean and median imputation, AWS ML best practices recommend using the median when features contain outliers. The mean is sensitive to extreme values and can be skewed significantly, leading to imputed values that are not representative of the typical data distribution.
In contrast, the median is robust to outliers, making it a better statistical estimator for central tendency in such datasets.
Amazon SageMaker Data Wrangler supports median imputation as a built-in transformation, enabling ML engineers to handle missing values consistently across large tabular datasets without custom code. This approach preserves all rows and columns while minimizing distortion caused by extreme values, which is particularly important for neural networks that are sensitive to input distributions.
Therefore, imputing missing values with the median value provides the most complete and statistically appropriate dataset for training.
NEW QUESTION # 36
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