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

Updated On: 8-Oct-2026
Page 3 of 33
PDF with 270 Questions

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



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.

After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

Which solution will meet these requirements?

  1. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
  2. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  3. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
  4. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.

Answer(s): C

Explanation:

Amazon SageMaker Data Wrangler is designed to preprocess, analyze, and visualize data efficiently. It provides built-in tools for anomaly detection, allowing the ML engineer to automatically identify anomalies in the dataset. Additionally, SageMaker Data Wrangler includes visualization capabilities to explore the data and results, meeting the requirements for anomaly detection and visualization in one integrated environment.



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.

The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

  1. Use AWS Glue to transform the categorical data into numerical data.
  2. Use AWS Glue to transform the numerical data into categorical data.
  3. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  4. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.

Answer(s): C

Explanation:

Transforming categorical data into numerical data is essential for ML models that require numerical input, as it allows the algorithm to process the categorical information effectively. Amazon SageMaker Data Wrangler provides an intuitive interface for data preparation, including built-in transformations like one-hot encoding and label encoding for categorical data. Using SageMaker Data Wrangler reduces operational overhead by offering an integrated environment to preprocess data without needing to write extensive code.



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.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

  1. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
  2. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
  3. Use AWS Glue DataBrew built-in features to oversample the minority class.
  4. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Answer(s): D

Explanation:

The Amazon SageMaker Data Wrangler balance data operation provides a built-in capability to handle class imbalance by oversampling the minority class or undersampling the majority class. This solution minimizes operational effort by offering an integrated, no-code/low-code approach to address the imbalance directly within SageMaker's data preparation workflow. It ensures that the dataset is balanced, improving the performance of the ML model.



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.

The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

  1. LightGBM
  2. Linear learner
  3. K-means clustering
  4. Neural Topic Model (NTM)

Answer(s): A



A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.

During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.

What could be the reason for the reduced F1 score?

  1. Concept drift occurred in the underlying customer data that was used for predictions.
  2. The model was not sufficiently complex to capture all the patterns in the original baseline data.
  3. The original baseline data had a data quality issue of missing values.
  4. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.

Answer(s): A

Explanation:

Concept drift occurs when the statistical properties of the data change over time, meaning the relationship between input features and the target variable in the production data differs from the data used during model training. This is a common reason for the degradation of a model's performance metrics, such as the F1 score, over time. In this case, changes in customer behavior or other external factors could cause the predictions to deviate from the actual outcomes, leading to a drop in the F1 score.



A company has a team of data scientists who use Amazon SageMaker AI notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker AI notebook instance.

The company needs to centralize management of the team's permissions.

Which solution will meet this requirement?

  1. Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
  2. Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
  3. Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
  4. Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.

Answer(s): A

Explanation:

By creating a single IAM role with the required permissions and attaching it to each SageMaker notebook instance, the company can centralize permission management. This solution ensures that all notebook instances share the same permissions, eliminating the need to manage permissions individually for each instance or user. It aligns with AWS best practices for role-based access control and reduces operational overhead.



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