Time-Window Aggregations and Downsampling in Time-Series Data Quiz

Test your foundational knowledge of grouping by time buckets, rollup techniques, and calculating percentiles in time-series data analysis.

1. Grouping Data in Time-Series

   Which SQL clause is commonly used to group time-series data into equal intervals, such as every 5 minutes?

   1. ORDER BY
   2. COLLECT BY
   3. GROUP BY
   4. CLUSTER BY
   5. ROLLUP

2. Purpose of Downsampling

   Why is downsampling applied to time-series data collected every second for visualization over several years?

   1. To duplicate raw values
   2. To increase the data volume
   3. To randomly delete data points
   4. To reduce storage and improve query speed
   5. To make data more complex

3. Understanding Time Buckets

   If data is grouped into 10-minute time buckets, what happens to data points within each bucket?

   1. They are separated into new tables
   2. Data is sorted alphabetically
   3. Each point is displayed individually
   4. All points are deleted except the first
   5. They are aggregated into a single summarized value

4. Aggregation Example

   Given temperature readings every minute, how could you summarize hourly trends using SQL?

   1. CLUSTER BY temperature values
   2. GROUP BY hour and apply AVG() to the temperature
   3. SELECT DISTINCT reading per second
   4. IGNORE PETITIONS during querying
   5. USE MINUTE ORDER ON data

5. Percentile Queries

   What is the result of using a percentile function, such as PERCENTILE(90), in a time bucket aggregation?

   1. A list of all unique values in each bucket
   2. The maximum value per bucket
   3. The average across all buckets
   4. The sum of all values per bucket
   5. The value below which 90% of values fall in each bucket

6. Choosing Bucket Size

   Which factor most influences the choice of time bucket size when aggregating time-series data?

   1. The username of the analyst
   2. The alphabetical order of measurements
   3. The color of data points
   4. The total number of columns
   5. The analysis granularity required

7. Rollup Concept

   What does a 'rollup' typically refer to in time-series data processing?

   1. Aggregating detailed data into summary values for larger time intervals
   2. Increasing the frequency of stored data points
   3. Rolling data visualizations into a single graph
   4. Formatting data in text files
   5. Repeating data queries continuously

8. AVG() Function Use

   When used with time window aggregations, what does the AVG() function calculate?

   1. The interval between each data point
   2. The alphabetical median of data labels
   3. The average of all collected values within each time bucket
   4. The sum of all time bucket durations
   5. The number of time buckets

9. Sample SQL Syntax

   Which of the following represents correct syntax to group by 1-hour intervals in SQL-like queries?

   1. ORDER BY hour_bucket(timestamp, 1)
   2. ROLLUP BY interval hour(timestamp)
   3. GROUP BY time_bucket('1 hour', timestamp)
   4. GROUP ON hour time, timestamp
   5. BUCKET HOUR GROUP(timestamp)

10. Result of Downsampling

    How does downsampling affect the resolution of a time-series data set?

    1. It removes all aggregate values
    2. It converts all data to categorical labels
    3. It reduces the data resolution by summarizing multiple points into aggregates
    4. It increases the data granularity by splitting points further
    5. It sorts the data by event name