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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
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?

Answer: C


NEW QUESTION # 28
An ML engineer is setting up a CI/CD pipeline for an ML workflow in Amazon SageMaker AI. The pipeline must automatically retrain, test, and deploy a model whenever new data is uploaded to an Amazon S3 bucket.
New data files are approximately 10 GB in size. The ML engineer also needs to track model versions for auditing.
Which solution will meet these requirements?

Answer: C

Explanation:
AWS documentation identifies SageMaker Pipelines as the native CI/CD service for ML workflows. Pipelines allow engineers to define automated steps for data processing, training, evaluation, and deployment, making them ideal for retraining models when new data arrives in Amazon S3.
For version tracking and auditing, SageMaker Model Registry is explicitly designed to manage model versions, metadata, approval status, and deployment history. This satisfies regulatory and audit requirements without custom tooling.
AWS Lambda is not suitable for handling large datasets (10 GB), and CodeBuild is not ML-aware and lacks built-in model governance. Manual notebook workflows do not meet CI/CD or automation requirements.
AWS best practices strongly recommend SageMaker Pipelines combined with the Model Registry for scalable, auditable, and production-grade ML CI/CD pipelines.
Therefore, Option B is the correct and AWS-verified solution.


NEW QUESTION # 29
A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

Answer: B

Explanation:
In AWS networking, security groups are stateful and allow-only, meaning they cannot explicitly deny traffic.
As a result, Option A is invalid. Network ACLs (NACLs), on the other hand, are stateless and support both allow and deny rules, making them the correct mechanism for blocking traffic from specific IP addresses.
Because the SageMaker AI domain is deployed in a public subnet, inbound traffic reaches the subnet before it reaches the resource. AWS documentation states that NACLs are evaluated at the subnet level and are ideal for implementing IP-based blocking rules.
Route tables control routing paths, not traffic filtering, so Option D is incorrect. Option C is unrelated to network security and does not block traffic.
AWS best practices clearly recommend using network ACL deny rules when an explicit block is required for a specific IP address at the subnet boundary.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 30
A company is developing a new ML model that uses the XGBoost algorithm. The company will train the model on data that is stored in an Amazon S3 bucket. The data is in a nested JSON format.
An ML engineer needs to convert the JSON files into a tabular format.
Which solution will meet this requirement with the LEAST operational overhead?

Answer: A

Explanation:
The AWS Glue PySpark Relationalize transform is purpose-built to convert nested JSON into tabular format with minimal operational overhead. It automates the flattening process without requiring custom code or complex infrastructure, making it the most efficient solution for preparing the data for XGBoost training.


NEW QUESTION # 31
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model ' s F1 score decreases significantly.
What could be the reason for the reduced F1 score?

Answer: C

Explanation:
Problem Description:
The F1 score, which is a balance of precision and recall, has decreased significantly. This indicates the model
' s predictions are no longer aligned with the real-world data distribution.
Why Concept Drift?
Concept drift occurs when the statistical properties of the target variable or features change over time. For example, customer behaviors or subscription cancellation patterns may have shifted, leading to reduced model accuracy.
Signs of Concept Drift:
Deviation in performance metrics (e.g., F1 score) over time.
Declining prediction accuracy for certain groups or scenarios.
Solution:
Monitor for drift using tools like SageMaker Model Monitor.
Regularly retrain the model with updated data to account for the drift.
Why Not Other Options?:
B: Model complexity is unrelated if the model initially performed well.
C: Data quality issues would have been detected during baseline analysis.
D: Incorrect ground truth labels would have resulted in a consistently poor baseline.
Conclusion: The decrease in F1 score is most likely due to concept drift in the customer data, requiring retraining of the model with new data.


NEW QUESTION # 32
......

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