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Amazon MLA-C01 Exam Questions
Free Amazon MLA-C01 Exam Actual Questions
Note: Premium Questions for MLA-C01 were last updated On Jul. 07, 2026 (see below)
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?
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.
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?
S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private.
Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account's IAM principals to provide the necessary permissions.
Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access.
This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.
A company is developing a generative AI conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model.
Which solution will meet these requirements?
The key requirement in this scenario is detecting customer intent without having any training data. According to AWS Machine Learning and Generative AI documentation, zero-shot learning is specifically designed for situations where labeled training data is unavailable. Zero-shot learning allows a pre-trained large language model (LLM) to perform tasks it has not been explicitly trained on by leveraging its general knowledge and language understanding.
Amazon Bedrock provides fully managed access to foundation models (FMs) and LLMs that support zero-shot and few-shot learning. By using an LLM from Amazon Bedrock, the company can directly infer customer intent from natural language inputs without building, training, or fine-tuning a custom model. This approach is ideal for conversational interfaces where rapid deployment and scalability are required.
Option A is incorrect because fine-tuning a sequence-to-sequence (seq2seq) model in Amazon SageMaker JumpStart still requires labeled training data. Since the company explicitly does not have training data, this option does not meet the requirement.
Option C is also incorrect because the Amazon Comprehend DetectEntities API is designed for named entity recognition (NER), such as detecting names, dates, locations, or monetary values. It does not perform intent detection and is not suitable for conversational AI intent classification.
Option D is partially misleading. While it is technically possible to run an LLM on Amazon EC2, this does not inherently solve the problem of intent detection without training data. Additionally, Amazon Bedrock already abstracts infrastructure management, scaling, and model hosting, making direct EC2 deployment unnecessary and less efficient.
Therefore, using an LLM from Amazon Bedrock with zero-shot learning is the most appropriate, scalable, and AWS-recommended solution for intent detection without training data.
A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model's performance in production.
Which solution will meet these requirements?
AWS ML best practices recommend A/B testing to validate model improvements in production while minimizing risk. By routing a controlled portion of live traffic (for example, 20%) to the new model and keeping the majority of traffic on the existing model, the company can directly compare real-world performance using the same data distribution.
This approach allows statistically meaningful comparison of business metrics such as customer retention, rather than relying solely on offline accuracy. It also limits potential negative impact if the new model underperforms in production.
Deploying the new model to 100% of traffic (Option A) introduces unnecessary risk. Offline analysis (Option C) does not reflect live user behavior. Alternating deployments (Option D) introduces confounding factors such as time-based effects.
Therefore, A/B testing is the correct solution.
An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.
The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.
What should the ML engineer do to transform the data and make the data suitable for training?
This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.
Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model's ability to learn disease-related patterns without discarding data.
PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.
Therefore, applying SMOTE to the training dataset is the correct approach.
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