Exam Certified Data Engineer Professional topic 1 question 49 discussion

Answer: D. Explanation: Lazy Evaluation: Spark employs lazy evaluation, meaning transformations are not executed until an action (e.g., display(), count(), collect()) is called. Using display() triggers the execution of the transformations up to that point. Caching Effects: Repeatedly executing the same cell can lead to caching, where Spark stores intermediate results. This caching can cause subsequent executions to be faster, not reflecting the true performance of the code. Why not B: Production-Sized Data and Clusters: While using production-sized data and clusters (as mentioned in option B) can provide insights into performance, it's not the only way to troubleshoot execution times. Proper testing can often be conducted on smaller datasets and clusters, especially during the development phase.

B and D are correct. The question says "which statements" which suggests us that this is a question with multiple choices

Both D and B are correct. But in real life some times clients dose not accept to gave you there production data to test easily. Also it says in B it is “the only way” ans this is not true for me So i will go with D

These people voting D have no reading comprehension.

Close env size volumes as possible so results make sense

D is correct

I will go with D

D is the correct answer A. Scala is the only language accurately tested using notebooks: Not true. Spark SQL and PySpark can be accurately tested in notebooks, and production performance doesn't solely depend on language choice. B. Production-sized data and clusters are necessary: While ideal, it's not always feasible for development. Smaller datasets and clusters can provide indicative insights. C. IDE and local Spark/Delta Lake: Local environments won't replicate production's scale and configuration fully. E. Jobs UI and Photon: True that Photon benefits scheduled jobs, but Jobs UI can track execution times regardless of Photon usage. However, Jobs UI runs might involve additional overhead compared to notebook cells. Option D addresses the specific limitations of using display() for performance measurement

Both B and D are correct statements. However, D is not an adjustment (see the question), it is just an afirmation which happens to be correct. B, however, is an adjustment, and it will definitely help with profiling.

As B not talking about how to deal with display() function. We know that way to testing performance for the whole notebook need to avoid using display as it is way to test the code and display the data

B is correct

D is correct

Calling display() forces a job to trigger - doesnt make sense display is used to display a df/table in tabular format, has nothing to do with a job trigger

D is correct

B is correct

Yes D is a True statement. But it does not answer the question. The ask is for "which adjustments will get a more accurate measure of how code is likely to perform in production". Answer D just describes why the chosen approach is not correct. It does not provide a solution.

Is it not B?