Quizes (#161)
* Adding content * Update en.json * Update README.md * Update TRANSLATIONS.md * Adding lesson tempolates * Fixing code files with each others code in * Update README.md * Adding lesson 16 * Adding virtual camera * Adding Wio Terminal camera capture * Adding wio terminal code * Adding SBC classification to lesson 16 * Adding challenge, review and assignment * Adding images and using new Azure icons * Update README.md * Update iot-reference-architecture.png * Adding structure for JulyOT links * Removing icons * Sketchnotes! * Create lesson-1.png * Starting on lesson 18 * Updated sketch * Adding virtual distance sensor * Adding Wio Terminal image classification * Update README.md * Adding structure for project 6 and wio terminal distance sensor * Adding some of the smart timer stuff * Updating sketchnotes * Adding virtual device speech to text * Adding chapter 21 * Language tweaks * Lesson 22 stuff * Update en.json * Bumping seeed libraries * Adding functions lab to lesson 22 * Almost done with LUIS * Update README.md * Reverting sunlight sensor change Fixes #88 * Structure * Adding speech to text lab for Pi * Adding virtual device text to speech lab * Finishing lesson 23 * Clarifying privacy Fixes #99 * Update README.md * Update hardware.md * Update README.md * Fixing some code samples that were wrong * Adding more on translation * Adding more on translator * Update README.md * Update README.md * Adding public access to the container * First part of retail object detection * More on stock lesson * Tweaks to maps lesson * Update README.md * Update pi-sensor.md * IoT Edge install stuffs * Notes on consumer groups and not running the event monitor at the same time * Assignment for object detector * Memory notes for speech to text * Migrating LUIS to an HTTP trigger * Adding Wio Terminal speech to text * Changing smart timer to functions from hub * Changing a param to body to avoid URL encoding * Update README.md * Tweaks before IoT Show * Adding sketchnote links * Adding object detection labs * Adding more on object detection * More on stock detection * Finishing stock counting * Tidying stuff * Adding wio purchase link * Updating Seeed logo * Update pi-proximity.md * Fix clean up link Fixes #145 * Moving attributions to a separate file * First draft of edge classifier * Adding extras * Moving folder * Adding lesson 11 questions * Image improvements * More image tweaks * Adding lesson 12 quiz * Quiz for lesson 13 * Adding quiz for lesson 14 * Lesson 15 and 16 quiz
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Jim Bennett
GitHub
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@ -1423,6 +1423,202 @@
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}
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{
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"id": 29,
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"title": "Lesson 15 - Train a fruit quality detector: Pre-Lecture Quiz",
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"quiz": [
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{
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"questionText": "Cameras can be used as IoT sensors",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "true"
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},
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{
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"answerText": "False",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "Fruit can be sorted using cameras",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "true"
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},
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{
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"answerText": "False",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "Images-based AI models are incredibly complex and time consuming to train, requiring hundreds of thousands of images:",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "false"
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},
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{
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"answerText": "False",
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"isCorrect": "true"
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}
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]
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}
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]
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},
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{
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"id": 30,
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"title": "Lesson 15 - Train a fruit quality detector: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "The technique custom vision uses to train a model with only a few images is called:",
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"answerOptions": [
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{
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"answerText": "Transformational learning",
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"isCorrect": "false"
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},
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{
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"answerText": "Transaction learning",
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"isCorrect": "false"
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},
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{
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"answerText": "Transfer learning",
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"isCorrect": "true"
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}
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]
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},
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{
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"questionText": "Image classifiers can be trained using:",
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"answerOptions": [
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{
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"answerText": "Only 1 image per tag",
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"isCorrect": "false"
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},
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{
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"answerText": "At least 5 images per tag",
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"isCorrect": "true"
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},
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{
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"answerText": "At least 50 images per tag",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "The hardware that allows ML models to be trained quickly, as well as making the graphics on out Xbox look amazing are called:",
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"answerOptions": [
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{
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"answerText": "PGU",
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"isCorrect": "false"
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},
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{
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"answerText": "GPU",
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"isCorrect": "true"
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},
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{
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"answerText": "PUG",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 31,
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"title": "Lesson 16 - Check fruit quality from an IoT device: Pre-Lecture Quiz",
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"quiz": [
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{
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"questionText": "IoT devices are not powerful enough to use cameras:",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "false"
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},
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{
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"answerText": "False",
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"isCorrect": "true"
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}
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]
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},
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{
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"questionText": "Camera sensors use film to capture images",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "false"
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},
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{
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"answerText": "False",
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"isCorrect": "true"
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}
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]
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},
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{
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"questionText": "Camera sensors send which type of data",
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"answerOptions": [
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{
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"answerText": "Digital",
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"isCorrect": "true"
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},
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{
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"answerText": "Analog",
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"isCorrect": "false"
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}
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]
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}
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]
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},
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{
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"id": 32,
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"title": "Lesson 16 - Check fruit quality from an IoT device: Post-Lecture Quiz",
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"quiz": [
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{
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"questionText": "A published version of a custom vision model is called an:",
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"answerOptions": [
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{
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"answerText": "Iteration",
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"isCorrect": "true"
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},
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{
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"answerText": "Instance",
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"isCorrect": "false"
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},
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{
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"answerText": "Iguana",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "When images are sent for classification, they then become available to retrain the model:",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "true"
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},
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{
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"answerText": "False",
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"isCorrect": "false"
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}
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]
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},
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{
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"questionText": "You don't need to use images captured from an IoT device to train the model as the cameras are as good quality as phone cameras:",
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"answerOptions": [
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{
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"answerText": "True",
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"isCorrect": "false"
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},
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{
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"answerText": "False",
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"isCorrect": "true"
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}
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]
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}
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]
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}
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]
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}
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