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ChatGPT passes the 2022 AP Computer Science A free response section

For fun, I had ChatGPT take the free response section of the 2022 AP Computer Science A exam. (The exam also has a multiple-choice section, but the College Board doesn't publish this.) It scored 32/36.

Methodology

For each question, I pasted in the full text of the question and took the response given.
I tried each question once and took the response given: no cherry-picking. For readability, I've added indentation in some cases, and included method signatures where they were provided in the question and ChatGPT only provided a body. I've added question numbers; any other comments are ChatGPT's.
Many questions have examples containing tables or diagrams; because those don't translate well to plain text, I excluded those tables/diagrams and any text that referenced them.
I excluded the initial instructions at the top of th

NOTE: commands and UI are deprecated

Content:

Negative Engineering
What is workflow orchestration?
Introduction to Prefect 2.0
First Prefect flow and Basics

Workflow Orchestration

Use Google Sheets as a "database" for simple or small size applications

References

Thanks to https://www.alec.fyi/set-up-google-sheets-apis-and-treat-sheets-like-a-database.html

Setup GCP

Reuse or create a project

A complete list of books, articles, blog posts, videos and neat pages that support Data Fundamentals (H), organised by Unit.

Formatting

If the resource is available online (legally) I have included a link to it. Each entry has symbols following it.

⨕⨕⨕ indicates difficulty/depth, from ⨕ (easy to pick up intro, no background required) through ⨕⨕⨕⨕⨕ (graduate level textbook, maths heavy, expect equations)
⭐ indicates a particularly recommended resource; 🌟 is a very strongly recommended resource and you should look at it.

OS.js v3 has been released

This is the "general" list of tasks for OS.js v3 development. I'm just adding things here as I go or things pop into mind. So if you see anything interesting and you want to pitch in, that'd be much appreciated.

See the following repositories for issues/todos that are not included here:

Client: https://github.com/os-js/osjs-client
Server: https://github.com/os-js/osjs-server
User Interfaces: https://github.com/os-js/osjs-gui
Dialog Interfaces: https://github.com/os-js/osjs-dialogs

	#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
	import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
	from torch.optim import AdamW
	from torch.utils.data import DataLoader, DistributedSampler
	from datasets import load_dataset
	from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

	STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

	def set_all_seed(seed):

	import torch
	import transformer_engine.pytorch.cpp_extensions as texcpp
	from transformer_engine.pytorch.module import get_workspace
	import transformer_engine_extensions as tex

	scale = 1.0

	meta = tex.FP8TensorMeta()
	meta.scale = torch.ones(1,dtype=torch.float32, device="cuda") * scale
	meta.scale_inv = torch.ones(1, dtype=torch.float32, device="cuda") / scale

	import torch
	import transformer_engine.pytorch as te
	from transformer_engine.common.recipe import Format, DelayedScaling
	import transformer_engine_extensions as tex
	import copy
	import math
	from typing import Callable, Optional

	def speedometer(
	module: torch.nn.Module,

	import math

	class Var:
	def __init__(self, val: float, local_gradients=()):
	self.val = val
	self.local_gradients = local_gradients
	self.grad = 0

	def backward(self, path_value: float = 1):
	for child_var, local_gradient in self.local_gradients:

	(function (context, trackingId, options) {
	const history = context.history;
	const doc = document;
	const nav = navigator \|\| {};
	const storage = localStorage;
	const encode = encodeURIComponent;
	const pushState = history.pushState;
	const typeException = 'exception';
	const generateId = () => Math.random().toString(36);
	const getId = () => {