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Amazon MLA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 1	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.
Thema 2	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.
Thema 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.
Thema 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 MLA-C01 Prüfungsfragen mit Lösungen (Q64-Q69):

64. Frage
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?

A. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
B. Apply statistics from a well-known dataset to normalize the production samples.
C. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
D. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.

Antwort: A

65. Frage
An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster.
The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an AWS Batch job that runs Apache Spark code on Amazon EC2 instances every week.Configure the Spark code to save the data from the EC2 instances to the second S3 bucket.
B. Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
C. Create an AWS Lambda function that runs Apache Spark code every week to merge and transform the data. Configure the Lambda function to connect to the initial S3 bucket and the DB cluster.
D. Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.

Antwort: D

66. Frage
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks.
The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
B. Export the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
C. Import the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
D. Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.

Antwort: D

Begründung:
The least operational overhead comes from directly importing the scikit-learn and TensorFlow models into SageMaker, rebuilding them using the respective prebuilt SageMaker frameworks, and then registering them in the SageMaker Model Registry. This leverages managed framework containers provided by SageMaker, avoids custom container management, and integrates seamlessly with the registry.

67. Frage
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

A. Create a SageMaker processing job by calling the SageMaker Python SDK.
B. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
C. Create a data flow in SageMaker Data Wrangler. Configure a transform step.
D. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.

Antwort: C

Begründung:
SageMaker Data Wrangler provides a no-code/low-code interface for preparing and transforming data, including dropping unnecessary columns. By creating a data flow and configuring a transform step, the ML engineer can easily remove correlated or unneeded columns from the Parquet file with minimal effort. This approach avoids the need for custom coding or managing additional infrastructure.

68. Frage
An ML engineer needs to use metrics to assess the quality of a time-series forecasting model.
Which metrics apply to this model? (Choose two.)

A. InferenceLatency
B. Recall
C. Root mean square error (RMSE)
D. Average weighted quantile loss (wQL)
E. LogLoss

Antwort: C,D

69. Frage
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