APCSP Khan Academy UPDATED ACTUAL Questions and CORRECT Answers
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APCSP Khan Academy
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APCSP Khan Academy
APCSP Khan Academy UPDATED
ACTUAL Questions and CORRECT
Answers
A high school computer science department wants to better understand how students study
for the AP CSP exam.
The CSP teachers send a survey to their 6 classes, asking these questions:
What grade are you in?
What CS classes di...
APCSP Khan Academy UPDATED
ACTUAL Questions and CORRECT
Answers
A high school computer science department wants to better understand how students study
for the AP CSP exam.
The CSP teachers send a survey to their 6 classes, asking these questions:
What grade are you in?
What CS classes did you take before CSP?
How many hours did you study?
How early did you start studying?
Which study resources did you use?
What did you earn on the exam?
They find the results very interesting, spread the survey to teachers all across the United
States, and collate the results in a central database.
Which of these analyses is an example of searching for patterns in a large data set? -
CORRECT ANSWER✔✔- Predicting the optimal start date and study hours, for a student
using a particular study resource and with a certain level of experience.
Company recruiters use applicant tracking systems to keep track of the resumes that
candidates send in for the job. Many applicant tracking systems use algorithms to
automatically rank the resumes, to help recruiters sift through large quantities of resumes.
Which of these algorithms would benefit the most from big data? - CORRECT
ANSWER✔✔- An algorithm that is trained on resumes from already hired applicants and
ranks based on similarity to those resumes.
Andy is using machine learning for an algorithm that classifies photos of restaurant meals
(like "sandwich", "curry", "salad"). He trains a neural network on a large open database of
photos of restaurant meals. He then tests the network on local restaurants and notices that the
Ethiopian restaurant meals aren't classified correctly.
What's the best way to improve the machine learning algorithm's ability to recognize
Ethiopian meals? - CORRECT ANSWER✔✔- He can add Ethiopian meals to the training
data set, by finding photos online, crowd-sourcing, or taking them himself.
, A hospital IT department is determining how much data storage capacity they will need to
store electronic health records for patients.
They start by making a list of the type of data that comes from each department:
Department Data Format & average size
Primary care Notes from patient chats with doctors 3 paragraphs per visit
Laboratory Test results A table with 20 rows and 3 columns
Radiology Imagery from scans (CT/PET/MRI) 64 1024x1024 grayscale images
Pharmacy Medication prescriptions Patient name/ID, medicine name, data
Which type of data is likely to require the most data storage capacity? - CORRECT
ANSWER✔✔- Imagery from scans (CT/PET/MRI)
In the modern age, supermarket chains can collect a huge amount of information about
customers and product inventory. They can analyze that data to help them understand their
customer base better and to make more informed decisions about store layout and marketing
campaigns.
Which of these analyses is an example of searching for patterns in a large data set? -
CORRECT ANSWER✔✔- Predicting the likelihood of any two products being purchased
together (e.g. if a customer buys Cheerios, there is a 30% chance they'll buy Strauss 2%
milk.)
Spam email are unsolicited messages sent in bulk, typically for advertising or phishing
purposes.
Email providers typically include a spam detection system, to automatically label and hide
emails that look like spam.
Screenshot of Gmail interface, with filters for "All Mail" and "Spam" messages
Which of these spam detection algorithms would benefit the most from big data? -
CORRECT ANSWER✔✔- An algorithm that computes the spam likelihood by computing
the similarity of an email to other spam emails.
Brittany is using machine learning for an algorithm that classifies social media posts
according to their sentiment ("positive", "negative", or "neutral").
She trains a neural network on a large open database of social media posts and tests the
network on her personal social media feed. She notices that it's mis-classifying the posts from
her teenage friends, who use different slang from her other friends.
What's the best way that Brittany can improve the machine learning algorithm's ability to
classify posts from teenagers? - CORRECT ANSWER✔✔- She can add social media posts
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