Courseiva
Exploratory Data AnalysiseasyMultiple ChoiceObjective-mapped

MLS-C01 Exploratory Data Analysis Practice Question

Network Topology
$ aws glue get-tabledatabase-name salesname transactionsRefer to the exhibit.```"Table": {"Name": "transactions","StorageDescriptor": {"Columns": [{"Name": "transaction_id", "Type": "string"},{"Name": "amount", "Type": "double"},{"Name": "timestamp", "Type": "timestamp"},{"Name": "store_id", "Type": "int"}],"Location": "s3://sales-data/transactions/","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": [{"Name": "year", "Type": "int"},{"Name": "month", "Type": "int"}

A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?

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

The first query uses a partition column (year), allowing partition pruning, while the second query does not.

The table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions, resulting in a longer execution time. Option A is incorrect because compression does not directly affect partition pruning; it reduces storage size but not scan time in this context. Option C is incorrect because the data format (Parquet) could help with columnar pruning, but the key difference here is partition pruning, not file format. Option D is incorrect because using SELECT * does not inherently cause slow performance; the lack of partition pruning is the main issue.

Answer analysis

Option-by-option breakdown

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

  • The data is compressed, and the first query benefits from compression.

    Why it's wrong here

    No compression is specified.

  • The first query uses a partition column (year), allowing partition pruning, while the second query does not.

    Why this is correct

    Partition pruning reduces data scanned.

  • The data is stored in Parquet format, which is optimized for columnar access.

    Why it's wrong here

    The table uses CSV (LazySimpleSerDe).

  • The second query is not optimized because it uses 'SELECT *'.

    Why it's wrong here

    SELECT * is not the main issue; partition pruning is.

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

About these practice questions

This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.