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Exploratory Data AnalysishardMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

Network Topology
aws glue get-tabledatabase-name mydbname mytableRefer to the exhibit.Output:"Table": {"Name": "mytable","StorageDescriptor": {"Columns": [{"Name": "id", "Type": "int"},{"Name": "name", "Type": "string"},{"Name": "price", "Type": "double"}],"Location": "s3://my-bucket/data/","InputFormat": "org.apache.hadoop.mapred.TextInputFormat","OutputFormat": "org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat","SerdeInfo": {"SerializationLibrary": "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe","Parameters": {"field.delim": ","},"PartitionKeys": []

Refer to the exhibit. A data scientist queries the table with 'SELECT COUNT(*) FROM mytable' in Athena and gets a result of 1000 rows. However, the scientist knows there are 1500 data files in the S3 location. What is the most likely reason for the discrepancy?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Some files may be empty or contain only headers, so they contribute 0 rows.

Athena counts rows from data files; if files are empty or contain only headers, they contribute 0 rows. With 1500 files and only 1000 rows, it is plausible that many files are empty or header-only, especially if the data pipeline produces such files. Option A is incorrect because Athena still parses lines even with a delimiter mismatch, treating each line as a row (though columns may be incorrect). Option B is incorrect because schema mismatch typically causes query errors, not silent skipping. Option D is false because Athena does not skip files based on size limits.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Some files may use a different delimiter (e.g., tab) and are not parsed correctly, resulting in zero rows from those files.

    Why it's wrong here

    Incorrect. Even with a different delimiter, Athena would still parse each line as a row, but columns may be wrong; it does not skip rows entirely.

  • The table schema does not match the data, causing some files to be skipped.

    Why it's wrong here

    Incorrect. Schema mismatch usually results in query failures, not silent omission of rows.

  • Some files may be empty or contain only headers, so they contribute 0 rows.

    Why this is correct

    Correct. Files that are empty or contain only a header row contribute zero data rows, explaining the discrepancy.

  • Athena skips files larger than a certain size to prevent scanning too much data.

    Why it's wrong here

    Incorrect. Athena scans all files regardless of size unless explicitly filtered via partitioning or WHERE clauses.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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