Amazon MLA-C01 Questions: Fosters Your Exam Passing Abilities [2026]

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q48-Q53):

NEW QUESTION # 48
An ML engineer wants to run a training job on Amazon SageMaker AI by using multiple GPUs. The training dataset is stored in Apache Parquet format.
The Parquet files are too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?

Answer: B

Explanation:
Large Parquet files can cause out-of-memory (OOM) issues during training if individual files exceed the memory capacity of the training instances. AWS documentation recommends repartitioning large datasets into smaller files to enable efficient streaming and parallel loading.
By using Apache Spark on Amazon EMR to repartition the Parquet files, the ML engineer can split the dataset into multiple smaller files that can be read incrementally during training. This approach avoids loading a single large file into memory and improves data parallelism.
Attaching larger EBS volumes increases storage capacity but does not solve memory constraints. Switching to memory-optimized instances increases cost and is not necessary when the dataset can be restructured.
SageMaker distributed data parallelism focuses on model parameter synchronization across GPUs, not dataset file size.
AWS best practices explicitly recommend partitioning large Parquet datasets to improve memory efficiency during training.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 49
An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space.
The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial.
Which solution will meet these requirements?

Answer: B

Explanation:
The requirement that each parameter trial is built based on the performance of the previous trial is the defining characteristic of Bayesian optimization. In Amazon SageMaker Automatic Model Tuning, Bayesian optimization uses prior trial results to intelligently select the next set of hyperparameters, making it far more efficient than grid or random search-especially in large, mixed search spaces.
This scenario includes both categorical (20) and continuous (7) hyperparameters and allows up to 1,000 training jobs, which is well within the supported limits of SageMaker AMT. Bayesian optimization natively supports mixed parameter types and is explicitly recommended by AWS for large, high-dimensional search spaces where exhaustive grid search is impractical.
Option A and D (grid search) do not meet the requirement because grid search evaluates combinations independently and does not learn from previous trials. Additionally, grid search becomes computationally infeasible as dimensionality increases.
Option B (random search) also evaluates trials independently and does not leverage previous results, violating the core requirement.
Therefore, defining both categorical and continuous parameters and using Bayesian optimization with a maximum of 1,000 jobs is the correct and AWS-recommended solution.


NEW QUESTION # 50
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

Answer: C


NEW QUESTION # 51
A company is training a deep learning model to detect abnormalities in images. The company has limited GPU resources and a large hyperparameter space to explore. The company needs to test different configurations and avoid wasting computation time on poorly performing models that show weak validation accuracy in early epochs.
Which hyperparameter optimization strategy should the company use?

Answer: A

Explanation:
When GPU resources are limited and the hyperparameter search space is large, AWS documentation strongly recommends Bayesian optimization combined with early stopping. Bayesian optimization uses past evaluation results to intelligently select the next set of hyperparameters to test, focusing exploration on promising regions of the search space rather than testing all combinations.
In Amazon SageMaker, Bayesian optimization is the default and recommended strategy for hyperparameter tuning jobs. It significantly reduces the number of training runs required compared to grid or random search, making it highly cost-efficient for deep learning workloads.
Early stopping further improves efficiency by terminating training jobs that show poor validation performance in early epochs. This prevents wasted GPU time on configurations that are unlikely to perform well. AWS explicitly documents early stopping as a key feature for controlling training cost and duration.
Grid search and exhaustive search are computationally expensive and impractical for large hyperparameter spaces. Manual tuning is slow, error-prone, and does not scale.
By combining Bayesian optimization with early stopping, the company can rapidly converge on high- performing hyperparameter configurations while minimizing resource usage.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 52
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers.
The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

Answer: B


NEW QUESTION # 53
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