SKILL BENCHMARK

Python Resource Optimization Proficiency (Advanced Level)

  • 22m
  • 22 questions
The Python Resource Optimization Proficiency (Advanced Level benchmark measures your ability to perform image transformation in OpenCV using advanced image operations to generate augmented or pre-processed images. You will be evaluated on your ability to implement operations for processing Faust stream data and use tables for fault-tolerance and stateful stream processing transformations. You will also be assessed on your knowledge of different windowing operation types, performing windowing operations, exposing app metrics using web views, and the differences between event time, ingestion time, and processing time. A learner who scores high on this benchmark demonstrates that they have the skills to perform advanced image operations using OpenCV, and can maintain state in tables and implement stream processing using windows operations in Faust.

Topics covered

  • access raw events and buffer events before performing operations
  • access tables from web views
  • aggregate data on a per-key, per-window basis
  • apply and blur noise in an image
  • apply morphological transformations such as erosion and dilation to emphasize specific features of an image
  • apply the Laplacian, Sobel, and Canny operators to detect edges in an image
  • compute aggregations on streaming data
  • contrast tumbling windows and hopping windows
  • enumerate entities in a stream with a single agent and with multiple agents
  • forward messages to destination topics
  • handle GET, PUT, POST, DELETE, and HTTP requests with web views
  • introduce a text element, polygon, and arrow to an OpenCV image
  • perform Gaussian and median blur operations to smoothen an image
  • perform group-by and items operations on streams
  • perform multiple grouping operations with the right agent structure
  • process streaming elements using multiple worker instances
  • recall the differences between event time, ingestion time, and processing time
  • recall the different kinds of sinks that can be used with a Faust agent
  • recall the different types of windows supported by Faust and their characteristics
  • save table state to an embedded RocksDB database
  • use the key index to iterate over keys, values, and items in windowed tables
  • use trained classifiers to detect eyes, faces, and people in images

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