CDP (Big Data Science) 4

Question 1

In machine learning pipelines for big data, what is 'feature engineering' primarily concerned with?

Accepted Answer

Transforming raw data into informative representations that improve model performance

Answer

Feature engineering involves selecting, transforming, and creating input variables from raw data to improve the predictive power of machine learning models.

Question 2

What does 'data lake' refer to in modern big data architectures?

Accepted Answer

A centralized repository that stores raw data in its native format until needed

Answer

A data lake stores vast amounts of raw, unstructured, semi-structured, and structured data in its native format, deferring structure definition until query time.

Question 3

Which concept describes the practice of moving compute to where data resides rather than moving data to compute in big data systems?

Accepted Answer

Data locality

Answer

Data locality minimizes network I/O by scheduling computation on nodes that already hold the required data, a core principle in Hadoop's MapReduce scheduler.

Question 4

In stream processing, what is the difference between 'event time' and 'processing time'?

Accepted Answer

Event time is when the event occurred at the source; processing time is when the system receives and processes it

Answer

Event time is the timestamp embedded in the event by the source system, while processing time is the wall-clock time when the stream processor receives the event.

Question 5

What is 'schema-on-read' as opposed to 'schema-on-write' in big data storage?

Accepted Answer

Schema-on-read applies structure to data at query time rather than enforcing it during ingestion

Answer

Schema-on-read defers schema application to query time, allowing raw data to be stored flexibly and interpreted with different schemas as needed.

CDP - Certified Data Professional Practice Test

CDP - Certified Data Professional Practice Test

CDP (Big Data Science) 4