AP Computer Science Principles Quiz: Extracting Information From Data

Question 1

1. Metadata allows the audio data to be compressed, reducing the file size and making the files load faster for playback.
2. Metadata, such as the release year stored with each file, can be used by software to filter and organize the entire collection.
3. Metadata changes the primary audio data to include a spoken announcement of the year at the beginning of each song file.
4. Metadata provides an encrypted key that allows the musician to prove they are the legal owner of the audio files.

Question 2

1. The primary audio data is altered, which will change the sound of the music when the file is played.
2. The file's metadata is changed, but the primary audio data that represents the music remains unchanged.
3. Both the metadata and the primary audio data are compressed to reduce the file's overall storage size.
4. A new piece of metadata is added to the file to track the history of all of its previous filenames for recovery.

Question 3

1. The total number of parks in the city and the total number of crimes reported during the year.
2. The names of the parks that are largest in size and the most common type of reported crime.
3. An identification of which parks have the highest or lowest crime rates, by linking crimes to park locations.
4. The average response time for police to arrive at a crime scene located inside a public park.

Question 4

1. The data collection method over-represents individuals who are technology experts, whose opinions may not reflect the general user population.
2. The survey questions might be excessively long, leading to incomplete data from some of the respondents who start but do not finish.
3. The company is collecting too much data from each respondent, which will make it difficult to process and analyze the results effectively.
4. The data are collected anonymously, which prevents the company from asking follow-up questions to the respondents to clarify their answers.

Question 5

1. The need to handle invalid and non-uniform data through a data cleaning process before performing calculations.
2. The issue of scalability, as the dataset is too large for a single person to analyze manually without assistance.
3. The ethical concern of collecting personally identifiable information such as birth year without explicit user consent.
4. The problem of finding correlations in the data that do not indicate a real causal relationship between variables.

Question 6

1. The public dataset is likely too large to be processed without using specialized parallel computing hardware.
2. The two datasets use different formats for location data, requiring a transformation or lookup table to link records.
3. The company's customer data contains personally identifiable information, which presents a significant security risk.
4. The data in both sources might contain a strong correlation that does not actually indicate a causal relationship.

Question 7

1. A trend of higher ridership during summer months.
2. A causal relationship between summer weather and the decision to use public transit.
3. A data-cleaning process that removed outlier passenger counts for certain months.
4. An anomaly in the data collection method during one specific year of the study.

Question 8

1. The data must be cleaned to remove invalid entries or artifacts before it can be used for any scientific analysis.
2. The data may contain biases introduced by the telescope's specific location and limited viewing angle.
3. The computational capacity of a single machine is insufficient, likely requiring parallel or distributed systems for processing.
4. The data must be converted from an analog signal to a digital format before it can be analyzed by computer programs.

Question 9

1. The total number of pixels that are predominantly blue in the photograph's image.
2. The specific time and GPS coordinates where the photograph was taken.
3. A compressed version of the image data used for creating a thumbnail preview.
4. The names of any individuals identified in the photograph through facial recognition.

Question 10

1. Sales peak in February and then steadily decline.
2. Sales dip in February, then rise through April.
3. Sales remain the same across all four months.
4. Sales are highest in January and lowest in April.

Question 11

1. Opening more ice cream shops in a city will cause the city to build more swimming pools.
2. Building more swimming pools in a city will cause more ice cream shops to open.
3. Cities with more ice cream shops are also likely to have more swimming pools, possibly due to a third factor like a warmer climate.
4. The data collected by the firm must be biased, as there is no logical connection between ice cream shops and swimming pools.

Question 12

1. A dataset of local weather patterns, including daily temperature and precipitation for the region.
2. A dataset from environmental agencies that contains daily air pollutant levels for the same region.
3. A dataset of census information showing the population density and demographics of the region.
4. A dataset of sales records from local pharmacies for over-the-counter cold remedies in the region.

Question 13

1. Data filtering, which involves removing all records that do not have a standard state abbreviation.
2. Data compression, which involves reducing the storage space required for the state information.
3. Data cleaning, which involves making the data uniform by converting all variations to a single, consistent format.
4. Data scaling, which involves adjusting the range of numerical data to fit within a common scale for analysis.

Question 14

1. Metadata describing the file types and creation dates of user-uploaded images and videos.
2. Patterns of community structure and user relationships within the platform's network.
3. A causal link proving that using the platform leads to forming new real-world friendships.
4. The raw text and image content from every individual user post made on the platform.

Question 15

1. Determining the frequency of a specific character's name appearing throughout the novel.
2. Finding the total number of words contained in the entire novel.
3. Identifying the publisher and the original publication date of the e-book.
4. Creating an automated summary of the novel's main plot points.

Question 16

1. The blank entries introduce a systematic bias, making the calculated average temperature appear higher than it actually is.
2. The incomplete data may prevent certain calculations, like a daily average, or require estimation methods to fill in missing values.
3. The blank entries are likely part of a lossless compression scheme used to store the data more efficiently.
4. The dataset is considered too large to be processed on a single computer due to the presence of the blank entries.

Question 17

1. Identify trends in overall sales from one month to the next.
2. Make connections between different products based on user behavior.
3. Clean the customer data to ensure all shipping addresses are valid.
4. Compress the purchase history data to save storage space on servers.

Question 18

1. The method is likely to under-represent teenagers who get more sleep, as they might arrive at school at a later time.
2. The data is invalid because the researcher did not survey every teenager in the entire world for the study.
3. The data is incomplete because it does not include information about what the teenagers ate for breakfast that morning.
4. The method relies on self-reported data from students, which is a common form of professional data cleaning.

Question 19

1. This information is metadata that allows for efficient searching and filtering without needing to open and read every PDF file.
2. This information is considered the primary data by the institute, while the PDF file is just a backup copy for reference.
3. Storing this information separately provides a form of lossless data compression for the large PDF files.
4. It is a security measure to prevent the PDF files from being altered, as changing the metadata would invalidate the file.

Question 20

1. The data only shows a correlation; it is possible that students who are already high-achieving are more likely to use a productivity app.
2. The data is inherently biased because it was collected by the same company that makes and sells the app.
3. The data is incomplete because it does not include students who use the app for less than five hours a week.
4. The data sample is too small to be meaningful, as it only includes students who report getting A's and B's.