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

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 DATEThis table is partitioned by the date column. A query is run with the following filter:longitude < 20 & longitude > -20Which statement describes how data will be filtered?
D) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
A) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
B) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
E) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

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

Actual exam question for Databricks's Databricks Certified Data Engineer Professional exam
Question #: 36
Topic #: 2
[All Databricks Certified Data Engineer Professional Questions]

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?

Show Suggested Answer Hide Answer
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.


by Elli at Jul 03, 2025, 11:29 PM

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Fallon
7 months ago
E seems inefficient, scanning all footers? No way!
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Tamar
7 months ago
D sounds right, it identifies files with potential matches.
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Taryn
7 months ago
A is correct, it uses partition stats!
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Jaclyn
7 months ago
I think B makes sense too, no file skipping.
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Maybelle
7 months ago
Wait, does the optimizer really not know about longitude?
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Ilda
8 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?
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German
8 months ago
I practiced a similar question, and I feel like the Delta Engine uses row-level stats for filtering. Is that option C?
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Belen
8 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?
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Maile
8 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?
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Wilda
9 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.
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Kyoko
9 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.
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Maile
9 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.
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Mariann
9 months 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.
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Gracia
9 months 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.
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Mila
11 months ago
I think C makes the most sense, as it mentions row-level statistics.
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Tamie
11 months ago
I'm not sure, but I think A could also be a possibility.
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Lai
11 months 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!
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Rasheeda
11 months 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!
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Lucy
10 months ago
It's interesting how the Delta Engine scans the parquet file footers.
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Gertude
10 months ago
I think option E is the most efficient way to filter the data.
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Freida
10 months ago
I agree, the Delta Engine is like a spy!
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Dusti
12 months ago
I disagree, I believe the correct answer is E.
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Magnolia
12 months 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.
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Florinda
11 months ago
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.
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Nobuko
11 months ago
I agree, option C makes the most sense in this scenario.
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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.
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Shantell
12 months ago
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.
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Tawanna
12 months ago
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.
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Serina
12 months ago
I agree, option D seems to be the most logical choice.
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Jaime
1 year ago
I think the answer is D.
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