MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is using Amazon QuickSight to explore a dataset with 10 million rows. The analyst wants to create a histogram of a numerical column. However, the query is taking too long. Which action should the analyst take to improve performance without losing accuracy?
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
✓
Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).
SPICE (Super-fast, Parallel, In-memory Calculation Engine) is Amazon QuickSight's in-memory engine that caches data, enabling fast query performance without losing accuracy. Importing the dataset into SPICE speeds up histogram computation while preserving full data. Option A is incorrect because using Athena with a limit clause reduces the number of rows, losing accuracy. Option B is incorrect because reducing bins changes histogram granularity, not necessarily improving performance and potentially losing detail. Option C is incorrect because sampling reduces accuracy by excluding data points.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the data source to Amazon Athena directly with a limit clause.
Why it's wrong here
Athena with limit would sample, losing accuracy.
- ✗
Reduce the number of bins in the histogram.
Why it's wrong here
Reducing bins may hide details but does not significantly improve query performance.
- ✗
Use a sample of the data (e.g., 1 million rows) for the histogram.
Why it's wrong here
Sampling reduces accuracy, especially for distribution shape.
- ✓
Import the dataset into SPICE (Super-fast, Parallel, In-memory Calculation Engine).
Why this is correct
SPICE accelerates queries by loading data into memory.
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