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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 3
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Topic 4
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q163-Q168):

NEW QUESTION # 163
An ML engineer is using Amazon SageMaker JumpStart to fine-tune a Llama 3.2 model for text generation.
The ML engineer is using an instruction-based fine-tuning method. The model uses 70 billion parameters.
Select the correct fine-tuning term from the following list to match each description. Select each term one time or not at all. (Select THREE.)
* Hyperparameter tuning
* Low-rank adaptation (LoRA)
* Fully Sharded Data Parallel (FSDP)
* Learning rate
* Int8 quantization

Answer:

Explanation:

Explanation:
* Int8 quantization
* Low-rank adaptation (LoRA)
* Fully Sharded Data Parallel (FSDP)
These three mappings align with AWS documentation for large-model training and optimization in SageMaker AI.
For 1 , the correct answer is Int8 quantization . AWS documentation explains that quantization reduces hardware and memory requirements by using a less precise data type for weights and activations , including INT8 formats. AWS Prescriptive Guidance also states that quantization reduces memory footprint by converting higher-precision weights to lower-precision formats such as FP16 to INT8 . That matches the description of reducing memory usage by representing model weights with lower precision.
For 2 , the correct answer is Low-rank adaptation (LoRA) . In practice, LoRA is a standard parameter- efficient fine-tuning (PEFT) method that updates only a small subset of trainable parameters instead of full- model fine-tuning. In SageMaker JumpStart workflows for large language models, LoRA is used specifically to make fine-tuning large models more efficient by training adapter-style low-rank updates rather than all original model weights. This matches the description of a PEFT technique that trains only a subset of model parameters.
For 3 , the correct answer is Fully Sharded Data Parallel (FSDP) . AWS documentation states that SageMaker model parallelism integrates with PyTorch FSDP , which shards model states across GPUs and supports large-scale distributed training. That directly matches the description of a distributed training technique used to share or shard model parameters across GPUs.


NEW QUESTION # 164
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 # 165
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

Answer: C


NEW QUESTION # 166
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: A

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 # 167
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 168
......

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