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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
  • 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 3
  • 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 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 (Q146-Q151):

NEW QUESTION # 146
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE- S3).
The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes.
After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors.
What should the ML engineer do to resolve this problem?

Answer: B


NEW QUESTION # 147
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

Answer: B

Explanation:
The key requirement is semantic search over text documents that already reside in Amazon S3. AWS provides Amazon Kendra, a fully managed service specifically designed for semantic and natural language search across unstructured text.
Amazon Kendra natively supports S3 connectors, which can ingest documents directly from an S3 bucket, automatically process the text, generate embeddings, and index the content for semantic retrieval. This removes the need for the company to manage embedding generation, vector storage, or similarity search infrastructure. Queries can be expressed in natural language, making Kendra well suited for RAG-style applications.
Option A and B require building and maintaining a custom embedding pipeline and do not provide a true vector similarity search engine using SQL. SageMaker Feature Store is not intended to function as a vector database for semantic search.
Option D is incorrect because Amazon Textract is an OCR service for extracting text from scanned documents and images; it does not support semantic search.
Therefore, ingesting documents using the Amazon Kendra S3 connector and querying Kendra is the correct and AWS-recommended solution.


NEW QUESTION # 148
A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket.
Data transformation scripts and queries are now taking much longer than they used to take.
An ML engineer must implement a solution to optimize the data for query performance.
Which solution will meet this requirement with the LEAST operational overhead?

Answer: D

Explanation:
AWS documentation strongly recommends using columnar storage formats to optimize analytical query performance on large datasets stored in Amazon S3. Apache Parquet is a columnar, compressed, and splittable file format that significantly improves query speed and reduces I/O compared to row-based formats such as CSV.
By using AWS Glue to convert CSV files into Parquet format, the company can achieve faster query execution with minimal operational overhead. Glue is fully managed, serverless, and integrates natively with S3, Amazon Athena, and Amazon Redshift Spectrum.
Option A does not improve query efficiency; splitting files still leaves the data in an inefficient row-based format. Option B may reduce data size but does not address the fundamental inefficiency of CSV for analytics. Option D introduces significant operational overhead because Amazon EMR requires cluster provisioning, scaling, and maintenance.
Therefore, converting CSV files to Apache Parquet using AWS Glue ETL is the most efficient and low- maintenance solution.


NEW QUESTION # 149
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 150
A company is developing ML models by using PyTorch and TensorFlow estimators with Amazon SageMaker AI. An ML engineer configures the SageMaker AI estimator and now needs to initiate a training job that uses a training dataset.
Which SageMaker AI SDK method can initiate the training job?

Answer: B

Explanation:
In the Amazon SageMaker Python SDK, the fit() method is used to start a training job after an estimator has been configured. AWS documentation explicitly states that once an estimator (such as PyTorch or TensorFlow) is defined with parameters like instance type, framework version, and hyperparameters, the fit() method is responsible for launching the training process.
The fit() method accepts the training data location (commonly an Amazon S3 URI) and initiates the managed training job on SageMaker infrastructure. SageMaker then provisions the required compute resources, stages the data, executes the training script, and stores model artifacts in Amazon S3.
The create_model() method is used after training to create a SageMaker model object from trained artifacts.
The deploy() method deploys a trained model to an endpoint for inference. The predict() method is used only after deployment to request predictions from an endpoint.
AWS documentation clearly separates these lifecycle steps and identifies fit() as the correct method to initiate training.
Therefore, Option A is the correct and AWS-verified answer.


NEW QUESTION # 151
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