Transform data and perform feature engineering.
7 practice questions under this official exam objective (MLA-C01), each with the answer key, an option-by-option explanation and instant feedback.
Questions under this objective
7 total · page 1 of 1Using Data Wrangler Corrupt Image Impulse Noise to Make a Fraud Camera Model Robust to Ima
A model detects automobile accidents from closed-circuit TV footage and performs well in training and validation but degrades in production because im
Processing Mixed CSV JSON XLSX Parquet Files in One S3 Folder with DataBrew and Outputting
An S3 bucket holds 1 TB of files from different sources with CSV, JSON, XLSX, and Apache Parquet all mixed in the same folder. The engineer must proce
Finding Duplicate Records in a Large Unstructured Dataset with AWS Glue FindMatches
A large unstructured dataset contains many duplicate records across several key attributes, and the goal is duplicate detection with the least code de
Best AWS Tool to Merge and Clean Datasets for ML?
Amazon SageMaker Data Wrangler is the purpose-built tool for importing multiple datasets, joining them into one data frame, and applying built-in clea
One-Hot Encoding Ad Campaign Color Schemes for a Neural Network Without Implying Order
An ML engineer is preparing categorical color-scheme data for a neural network that predicts advertising campaign success. Colors are nominal categori
Fixing Class Imbalance with the SageMaker Data Wrangler Balance Data Oversampling Operatio
Before training the fraud detection model, the engineer must resolve the class imbalance in the dataset with the least operational effort. Both AWS Gl
Encoding Categorical Features with SageMaker Data Wrangler for the Fraud Detection Trainin
The fraud detection training dataset contains both categorical and numerical features and must be prepared to maximize model accuracy with the least o