Secure 100% Exam Results with Amazon MLA-C01 Practice Questions [2026]

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q13-Q18):

NEW QUESTION # 13
A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?

Answer: D

Explanation:
Amazon SageMaker supports direct file system access for training jobs through Amazon FSx. AWS documentation states that FSx for NetApp ONTAP file systems can be mounted directly to SageMaker training instances when they are located in the same VPC.
Mounting the FSx file system allows SageMaker to stream large datasets efficiently without copying data into Amazon S3. This is ideal for very large datasets such as 6 TB of image data and avoids unnecessary storage duplication.
Options involving SageMaker Data Wrangler are intended for data preparation and exploration, not large- scale training data access. Mountpoint for Amazon S3 is not required and introduces additional complexity.
Therefore, Option A is the correct and AWS-aligned solution.


NEW QUESTION # 14
An ML engineer wants to run a training job on Amazon SageMaker AI. The training job will train a neural network by using multiple GPUs. The training dataset is stored in Parquet format.
The ML engineer discovered that the Parquet dataset contains files too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?

Answer: B

Explanation:
The issue is caused by oversized Parquet files that cannot be efficiently read into memory during training. The most effective and scalable solution is to repartition the dataset into smaller Parquet files.
AWS best practices for large-scale ML training recommend optimizing data layout, not simply increasing memory. By using Apache Spark on Amazon EMR, the ML engineer can repartition the Parquet files into smaller chunks that can be streamed and processed efficiently by SageMaker training jobs.
Attaching EBS volumes (Option A) increases storage capacity but does not solve in-memory constraints.
Changing to memory-optimized instances (Option C) increases cost and does not address long-term scalability. SMDDP (Option D) distributes gradients and computation, not dataset file sizes.
Therefore, repartitioning the Parquet files is the correct solution.


NEW QUESTION # 15
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?

Answer: A


NEW QUESTION # 16
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?

Answer: C

Explanation:
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.


NEW QUESTION # 17
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C


NEW QUESTION # 18
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