Discuss Databricks Databricks Certified Data Engineer Professional Exam Topic 2 Question 36

A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

AStatistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

BNo file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

CThe Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

DStatistics in the Delta Log will be used to identify data files that might include records in the filtered range.

EThe Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Suggested Answer: D

This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''Data skipping'' section.

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30 comments · newest first

Fallon 10 months ago

E seems inefficient, scanning all footers? No way!

Tamar 10 months ago

D sounds right, it identifies files with potential matches.

Taryn 10 months ago

A is correct, it uses partition stats!

Jaclyn 10 months ago

I think B makes sense too, no file skipping.

Maybelle 10 months ago

Wait, does the optimizer really not know about longitude?

Ilda 11 months ago

I’m a bit confused about how the parquet file footers come into play. I thought they were only for reading data, so maybe option E isn't right?

German 11 months ago

I practiced a similar question, and I feel like the Delta Engine uses row-level stats for filtering. Is that option C?

Belen 11 months ago

I remember something about Delta Lake using statistics in the log, but I'm not sure if it's for identifying partitions or files. Could it be option D?

Maile 11 months ago

I think the optimizer might not know how to skip files since the filter is on longitude, not the partitioned date. So maybe option B?

Wilda 12 months ago

This seems straightforward to me. The Delta Engine should be able to use the partition statistics to identify the relevant data files and avoid scanning unnecessary data. I'll go with the option that mentions using the Delta Log to identify the files that might include the filtered records.

Kyoko 12 months ago

I'm a bit confused by the options here. It's not clear to me whether the Delta Engine will use row-level statistics or just partition-level statistics to filter the data. I'll need to think this through carefully and make sure I understand the differences between the options.

Maile 12 months ago

Okay, I think I've got this. The key is that the table is partitioned by date, so the Delta Engine should be able to use the partition statistics to skip over any irrelevant data files. I'll select the option that mentions using the Delta Log to identify the relevant partitions.

Mariann 1 year ago

Hmm, this is a tricky one. I'm not sure if the optimizer can infer anything about the longitude values based on the partitioned date column. I'll need to review the Delta Lake documentation on partition pruning.

Gracia 1 year ago

This question seems to be testing our understanding of how Delta Lake handles filtering data. I'll need to think carefully about the relationship between the partitioned column and the filter criteria.

Mila 1 year ago

I think C makes the most sense, as it mentions row-level statistics.

Tamie 1 year ago

I'm not sure, but I think A could also be a possibility.

Lai 1 year ago

I'm stuck between A and D. It's like a game of 'which partition will get picked?' I hope the exam doesn't leave me 'partitioned' from the right answer!

Rasheeda 1 year ago

Haha, the Delta Engine is like a spy, reading the 'parquet file footers' to find the right rows. Option E is definitely the most fun answer!

Lucy 1 year ago

↳ replying to Rasheeda

It's interesting how the Delta Engine scans the parquet file footers.

Gertude 1 year ago

↳ replying to Rasheeda

I think option E is the most efficient way to filter the data.

Freida 1 year ago

↳ replying to Rasheeda

I agree, the Delta Engine is like a spy!

Dusti 1 year ago

I disagree, I believe the correct answer is E.

Magnolia 1 year ago

Option C seems more accurate to me. The Delta Engine should use the row-level statistics in the transaction log to find the files that match the filter criteria.

Florinda 1 year ago

↳ replying to Magnolia

I think option D could also be a possibility, as it mentions using statistics in the Delta Log to identify data files that match the filter criteria.

Nobuko 1 year ago

↳ replying to Magnolia

I agree, option C makes the most sense in this scenario.

Sarah 1 year ago

I think option D is the correct answer. The Delta Log contains statistics that can be used to identify the relevant data files for the filter.

Shantell 1 year ago

↳ replying to Sarah

I'm not sure, but option E sounds like it could be a valid approach as well, scanning the parquet file footers for rows that meet the filter criteria.

Tawanna 1 year ago

↳ replying to Sarah

I think option A could also be a possibility, as statistics in the Delta Log can help identify partitions with data in the filtered range.

Serina 1 year ago

↳ replying to Sarah

I agree, option D seems to be the most logical choice.

Jaime 1 year ago

I think the answer is D.

Databricks Certified Data Engineer Professional Exam - Topic 2 Question 36 Discussion