MLA-C01 Practice Questions
AWS Certified Machine Learning Engineer - Associate
Last Update 4 days ago
Total Questions : 241
Dive into our fully updated and stable MLA-C01 practice test platform, featuring all the latest AWS Certified Associate exam questions added this week. Our preparation tool is more than just a Amazon Web Services study aid; it's a strategic advantage.
Our free AWS Certified Associate practice questions crafted to reflect the domains and difficulty of the actual exam. The detailed rationales explain the 'why' behind each answer, reinforcing key concepts about MLA-C01. Use this test to pinpoint which areas you need to focus your study on.
A healthcare analytics company wants to segment patients into groups that have similar risk factors to develop personalized treatment plans. The company has a dataset that includes patient health records, medication history, and lifestyle changes. The company must identify the appropriate algorithm to determine the number of groups by using hyperparameters.
Which solution will meet these requirements?
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?
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?
An ML engineer at a credit card company built and deployed an ML model by using Amazon SageMaker AI. The model was trained on transaction data that contained very few fraudulent transactions. After deployment, the model is underperforming.
What should the ML engineer do to improve the model’s performance?
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?
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?
An ML engineer is building a model to predict house and apartment prices. The model uses three features: Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment.
The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment.
Which solution will meet these requirements?
A company is uploading thousands of PDF policy documents into Amazon S3 and Amazon Bedrock Knowledge Bases. Each document contains structured sections. Users often search for a small section but need the full section context. The company wants accurate section-level search with automatic context retrieval and minimal custom coding.
Which chunking strategy meets these requirements?
A company wants to share data with a vendor in real time to improve the performance of the vendor ' s ML models. The vendor needs to ingest the data in a stream. The vendor will use only some of the columns from the streamed data.
Which solution will meet these requirements?
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?
