AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Questions
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A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?
Correct Answer: A
Dynamic data masking allows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientist can access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.
A company is developing an application that reads animal descriptions from user prompts and generates images based on the information in the prompts. The application reads a message from an Amazon Simple Queue Service (Amazon SQS) queue. Then the application uses Amazon Titan Image Generator on Amazon Bedrock to generate an image based on the information in the message. Finally, the application removes the message from SQS queue.
Which IAM permissions should the company assign to the application ' s IAM role? (Select TWO.)
Correct Answer: A, C
To generate images, the application must invoke a foundation model hosted on Amazon Bedrock. Invoking the Amazon Titan Image Generator requires the bedrock:InvokeModel permission. Read-only permissions such as bedrock:Get* do not allow inference execution and are insufficient.
The application also interacts with Amazon Simple Queue Service. To process messages, the application must be able to receive messages from the queue and delete them after successful processing. These actions require sqs:ReceiveMessage and sqs:DeleteMessage.
sqs:GetQueueAttributes alone does not allow message consumption. SageMaker permissions are unrelated because Bedrock is a separate service.
Therefore, permissions to invoke the model and receive/delete SQS messages are required.
An ML engineer wants to run a training job on Amazon SageMaker AI. The training job will train a neural network by using multiple GPUs. The training dataset is stored in Parquet format.
The ML engineer discovered that the Parquet dataset contains files too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?
Correct Answer: B
The issue is caused by oversized Parquet files that cannot be efficiently read into memory during training. The most effective and scalable solution is to repartition the dataset into smaller Parquet files.
AWS best practices for large-scale ML training recommend optimizing data layout, not simply increasing memory. By using Apache Spark on Amazon EMR, the ML engineer can repartition the Parquet files into smaller chunks that can be streamed and processed efficiently by SageMaker training jobs.
Attaching EBS volumes (Option A) increases storage capacity but does not solve in-memory constraints. Changing to memory-optimized instances (Option C) increases cost and does not address long-term scalability. SMDDP (Option D) distributes gradients and computation, not dataset file sizes.
Therefore, repartitioning the Parquet files is the correct solution.
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?
Correct Answer: D
Scenario: The dataset contains duplicate records that need to be detected with minimal code development.
Why FindMatches in AWS Glue?
Purpose-Built for Deduplication: The FindMatches transform in AWS Glue is specifically designed to identify duplicate records in structured or semi-structured datasets.
Machine Learning-Based: It uses ML to identify duplicates based on configurable thresholds and provides flexibility for tuning accuracy.
Low Code Overhead: Minimal development effort is required as Glue provides an interactive console for configuring and running FindMatches transforms.
Steps to Implement:
Prepare the Data: Upload the unstructured dataset to an S3 bucket and define a schema if needed.
Create a Glue Job:
Use the AWS Glue Studio to create a job and select the FindMatches transform.
Specify key attributes for deduplication.
Run and Evaluate: Execute the Glue job, and review the results for duplicates.
Resolve Duplicates: Export results to an S3 bucket or process them as needed.
An ML engineer is training an ML model to identify medical patients for disease screening. The tabular dataset for training contains 50,000 patient records: 1,000 with the disease and 49,000 without the disease.
The ML engineer splits the dataset into a training dataset, a validation dataset, and a test dataset.
What should the ML engineer do to transform the data and make the data suitable for training?
Correct Answer: B
This dataset shows severe class imbalance, with only 2% of records representing patients with the disease. AWS ML best practices recommend correcting imbalance only in the training dataset, while keeping validation and test sets representative of real-world distributions.
Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples of the minority class by interpolating between existing minority examples. This improves the model’s ability to learn disease-related patterns without discarding data.
PCA is a dimensionality reduction method, not an oversampling technique. Oversampling the majority class worsens imbalance. Altering the test dataset would invalidate evaluation results.
Therefore, applying SMOTE to the training dataset is the correct approach.