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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
  • 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
  • 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q167-Q172):

NEW QUESTION # 167
An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation.
The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time.
What should the ML engineer do to confirm or deny this hypothesis?

Answer: D


NEW QUESTION # 168
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Answer:

Explanation:

Explanation:
* City (name):One-hot encoding
* Type_year (type of home and year the home was built):Feature splitting
* Size of the building (square feet or square meters):Standardized distribution
* City (name): One-hot encoding
* Why?The "City" is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for eachunique category (e.g., cities like "New York" or "Los Angeles"), which the model can interpret.
* Type_year (type of home and year the home was built): Feature splitting
* Why?"Type_year" combines two pieces of information into one column, which could confuse the model. Feature splitting separates this column into two distinct features: "Type of home" and
"Year built," enabling the model to process each feature independently.
* Size of the building (square feet or square meters): Standardized distribution
* Why?Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.


NEW QUESTION # 169
Hotspot Question
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store.
Each step should be selected one time. (Select and order three.)
- Access the store to build datasets for training.
- Create a feature group.
- Ingest the records.

Answer:

Explanation:


NEW QUESTION # 170
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

Answer: B


NEW QUESTION # 171
An ML engineer is training an ML model to identify people's health risk based on 20 features and
1 target. The target class has two values:
- Likely to have health risk (positive class)
- Unlikely to have health risk (negative class)
The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features.
The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges.
What should the ML engineer do to correct this data imbalance?

Answer: D

Explanation:
A DPL of +0.9 indicates that the positive class is heavily overrepresented in the 40-45 age range compared to other age ranges. To correct this imbalance, the solution is to undersample the positive class within the 40-45 range, reducing its dominance and improving fairness in the dataset.


NEW QUESTION # 172
......

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