MLA-C01 - AWS Certified Machine Learning Engineer-Associate MLAC01 Exam - Exam Questions

Sep 20, 2026

Practice exam questions for the AWS Certified Machine Learning Engineer-Associate (MLA-C01) covering Amazon SageMaker and related AWS services. Topics include making image-based training data accessible from Amazon FSx for NetApp ONTAP within the same VPC, triggering SageMaker pipelines when new S3 data arrives with minimal operations, improving fraud detection on unseen transactions using XGBoost hyperparameters, selecting metrics for model recalibration in binary classification, and choosing orchestration/governance options using SageMaker Experiments or lineage tracking.

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Question 78

Question 79

Question 80

Question 81

Question 82

Question 83

Question 84

Braindump2go Guarantee All Exams 100% Pass! 􀂾 Vendor: Amazon 􀂾 Exam Code: MLA-C01 􀂾 Exam Name: AWS Certified Machine Learning Engineer-Associate MLAC01 Exam 􀂾 New Updated Questions from Braindump2go 􀂾 (Updated in November/2025) Visit Braindump2go and Download Full Version MLA-C01 Exam Dumps QUESTION 78 A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6衣of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM) . The SVM is in the same VPC as SageMaker. An ML engineer must make the training data accessible for ML models that are in the SageMaker environment. Which solution will meet these requirements? A. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance. B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system. C. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system. D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system. Answer: A QUESTION 79 A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days. The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket. Which solution will meet these requirements with the LEAST operational effort? A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training. B. Create an AWS Lambda function that scans the S3 bucket. Program the Lambda function to initiate the pipeline when new data is uploaded. C. Create an Amazon EventBridge rule that has an event pattern that matches the S3 upload. Configure the pipeline as the target of the rule. D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded. Answer: C QUESTION 80 An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate. During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions. What should the ML engineer do to improve the fraud detection for new transactions? Braindump2go Guarantee All Exams 100% Pass! A. Increase the learning rate. B. Remove some irrelevant features from the training dataset. C. Increase the value of the max_depth hyperparameter. D. Decrease the value of the max_depth hyperparameter. Answer: D QUESTION 81 A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements. Which metric should the ML engineer use for the model recalibration? A. Accuracy B. Precision C. Recall D. Specificity Answer: A QUESTION 82 A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG) . The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications. Which solution will meet these requirements? A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance ve

FAQs

How can training data on FSx for NetApp ONTAP be accessed by SageMaker models in the same VPC?

Mount the FSx for ONTAP file system as a volume to the SageMaker instance.

What is the least operational-effort way to run a SageMaker pipeline when new S3 data is uploaded?

Use an Amazon EventBridge rule that matches the S3 upload event and sets the pipeline as the target.

To improve fraud detection for new unseen transactions using SageMaker XGBoost, what should be adjusted?

Decrease the value of the max_depth hyperparameter.

Sep 20, 2026

Uploaded by Riley West