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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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.
Topic 4	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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q202-Q207):

NEW QUESTION # 202
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use random forests to classify sentiments of the chat conversations.
B. Use Amazon Rekognition to analyze sentiments of the chat conversations.
C. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
D. Use Amazon Comprehend to analyze sentiments of the chat conversations.

Answer: D

NEW QUESTION # 203
An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

A. Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.
B. Use Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.
C. Train the deep learning model directly on the raw data. Let the model infer the labels by itself.
D. Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements.
E. Use Amazon Rekognition to automatically label the dataset.

Answer: A,D

Explanation:
To achieve the highest labeling accuracy with controlled employee-only access, the ML engineer should use Amazon SageMaker Ground Truth to define the annotation job and then assign it to a private workforce of employees for labeling and review. This ensures high-quality, secure labeling restricted to authorized personnel.

NEW QUESTION # 204
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?

A. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
B. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
C. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
D. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.

Answer: B

NEW QUESTION # 205
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?

A. Configure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.
B. Configure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache Parquet format.
C. Configure an Amazon EMR cluster to process the data that is in the S3 bucket.
D. Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.

Answer: B

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 # 206
Hotspot Question
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
- An S3 event notification invokes the pipeline when new data is
uploaded.
- S3 Lifecycle rule invokes the pipeline when new data is uploaded.
- SageMaker retrains the model by using the data in the S3 bucket.
- The pipeline deploys the model to a SageMaker endpoint.
- The pipeline deploys the model to SageMaker Model Registry.

Answer:

Explanation:

NEW QUESTION # 207
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