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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 3 )

Updated On: 7-Feb-2026
Page 3 of 47
PDF with 230 Questions

HOTSPOT

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.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct steps, in order, are:
4. Create a feature group.
A feature group defines the schema and structure for the features, serving as a container for storing and organizing features.
5. Ingest the records.
Populate the feature group by ingesting data records, which include the features and their associated values.
6. Access the store to build datasets for training.
Retrieve features from the Feature Store to construct datasets for model training.



HOTSPOT

A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (CI/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.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:

The correct steps, in order, are:
7. An S3 event notification invokes the pipeline when new data is uploaded. Set up an S3 event notification to trigger the pipeline when new training data is added to the S3 bucket.

8. SageMaker retrains the model by using the data in the S3 bucket. The pipeline should include a step to retrain the ML model using the new data in the S3 bucket.

9. The pipeline deploys the model to a SageMaker endpoint. After retraining, the pipeline deploys the updated model to a SageMaker endpoint for inference.



HOTSPOT

An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).

Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all.

· Embedding
· Retrieval Augmented Generation (RAG)
· Temperature
· Token

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct terms for each description are:
10. Text representation of basic units of data processed by LLMs - Token Tokens are the basic units of text (such as words or subwords) that LLMs process.

11. High-dimensional vectors that contain the semantic meaning of text - Embedding Embeddings are numerical representations of text in high-dimensional space, capturing semantic meaning.
12. Enrichment of information from additional data sources to improve a generated response - Retrieval Augmented Generation (RAG)
RAG involves retrieving relevant information from external data sources to enhance the quality of generated responses.



HOTSPOT

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.

Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




The correct feature engineering techniques for each feature are:
13. City (name) - One-hot encoding
The city name is a categorical feature, so one-hot encoding is used to convert it into a binary vector representation for the model.
14. Type_year (type of home and year the home was built) - Feature splitting This combined feature can be split into two separate features: "type of home" and "year the home was built," for more meaningful analysis.
15. Size of the building (square feet or square meters) - Logarithmic transformation Logarithmic transformation can be applied to normalize the distribution if the size has a skewed distribution.



Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Which AWS service or feature can aggregate the data from the various data sources?

  1. Amazon EMR Spark jobs
  2. Amazon Kinesis Data Streams
  3. Amazon DynamoDB
  4. AWS Lake Formation

Answer(s): D






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