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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #157

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you

The correct answer is C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed. With hundreds of millions of records, importing the full dataset into a notebook (B) would exhaust memory and compute resources. BigQuery is purpose-built for large-scale aggregation queries, making it ideal for computing descriptive statistics (mean, median, mode) on the full da

Submitted by jian89· Apr 18, 2026Data processing and feature engineering

Question

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time. You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

Options

  • AVisualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-
  • BSpin up a Vertex Al Workbench user-managed notebooks instance and import the dataset. Use
  • CUse BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed
  • DUse BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the

How the community answered

(41 responses)
  • A
    17% (7)
  • B
    7% (3)
  • C
    71% (29)
  • D
    5% (2)

Explanation

With hundreds of millions of records, importing the full dataset into a notebook (B) would exhaust memory and compute resources. BigQuery is purpose-built for large-scale aggregation queries, making it ideal for computing descriptive statistics (mean, median, mode) on the full dataset efficiently and cost-effectively. Vertex AI Workbench notebooks are then used for complex statistical hypothesis testing and time-series visualizations, operating on the aggregated or sampled outputs from BigQuery rather than the raw dataset. Option D omits the hypothesis testing requirement. Option A uses Data Studio for tasks it is not optimized for (statistical testing).

Topics

#Data processing#Statistical analysis#BigQuery#Vertex AI Workbench

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