Exam Certified Associate Developer for Apache Spark topic 1 question 31 discussion

To quickly return an approximation for the number of distinct values in column division in DataFrame storesDF, the most efficient code block to use would be: B. storesDF.agg(approx_count_distinct(col("division"), 0.01).alias("divisionDistinct")) Using the approx_count_distinct() function allows for an approximate count of the distinct values in the column without scanning the entire DataFrame. The second parameter passed to the function is the maximum estimation error allowed, which in this case is set to 0.01. This is a trade-off between the accuracy of the estimate and the computational cost. Option C may still be efficient but with a larger estimation error of 0.15. Option A and D are not correct as they do not specify the estimation error, which means that the function would use the default value of 0.05. Option E specifies an estimation error of 0.05, but a smaller error of 0.01 is a better choice for a more accurate estimate with less computational cost.

C is correct because it will provide the fastest approximate count with a standard deviation of 0.15

B. storesDF.agg(approx_count_distinct(col("division"), 0.01).alias("divisionDistinct")) Explanation: approx_count_distinct(col("division"), 0.01): This uses the approx_count_distinct function to approximate the number of distinct values in the "division" column with a relative error of 1%. The smaller the relative error, the more accurate the approximation, but it may require more resources. .alias("divisionDistinct"): This renames the result column to "divisionDistinct" for better readability. So, the correct answer is: B. storesDF.agg(approx_count_distinct(col("division"), 0.01).alias("divisionDistinct"))

C The code block that will most quickly return an approximation for the number of distinct values in column `division` in DataFrame `storesDF` is **C**, `storesDF.agg(approx_count_distinct(col("division"), 0.15).alias("divisionDistinct"))`. The `approx_count_distinct` function can be used to quickly estimate the number of distinct values in a column by using a probabilistic data structure. The second parameter of the `approx_count_distinct` function specifies the maximum estimation error allowed, with a smaller value resulting in a more accurate but slower estimation. In this case, an error of 0.15 is specified, which will result in a faster but less accurate estimation than the other options.

C - the less accurate the calculation, the faster it is

A. https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.approx_count_distinct.html

While not an option I would use, the question says most quickly (relatively), and this will be the fastest. Note that a 15% error is too high.

The higher the relative error parameter, the less accurate and faster. The lower the relative error parameter, the more accurate and slower.