MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is exploring a dataset with a timestamp column and wants to resample the data to a consistent 1-hour frequency. The data is irregularly spaced. Which approach is most efficient using AWS services?
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
✓
Use Amazon SageMaker Processing with a custom script
Amazon SageMaker Processing jobs allow custom scripts (e.g., using pandas resample) to handle irregular time series, and they are fully managed. Option A is wrong because Amazon EMR with Spark requires cluster management and is more complex for simple resampling. Option B is wrong because AWS Glue with built-in transforms is more suited for batch ETL but may be overkill for this task. Option C is wrong because Amazon Athena with SQL window functions is a query engine and cannot resample easily.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon EMR with Spark
Why it's wrong here
Amazon EMR with Spark is not the most efficient choice for simple time-series resampling due to the inherent overhead of provisioning and managing a distributed cluster. While Spark can handle time-series operations, this approach introduces unnecessary latency and operational complexity for a task that can be handled more directly. It would be highly suitable for complex, large-scale data transformations, custom aggregations, or machine learning workloads requiring distributed processing across massive datasets, where its powerful capabilities justify the infrastructure.
- ✗
Use AWS Glue with built-in transforms
Why it's wrong here
AWS Glue is more suited for batch ETL but may be overkill for simple resampling.
- ✗
Use Amazon Athena with SQL window functions
Why it's wrong here
Amazon Athena is a query engine and cannot resample irregular time series easily.
- ✓
Use Amazon SageMaker Processing with a custom script
Why this is correct
Amazon SageMaker Processing jobs allow custom scripts (e.g., using pandas resample) to handle irregular time series, and they are fully managed.
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