2.1 Identify Data Trends, Patterns, and Insights by Using BigQuery and Jupyter Notebooks ASSOCIATE-DATA-PRACTITIONER Practice Questions (Page 2)

Questions 6–10

1. 6foundation · medium

   A data analyst needs to produce a summary report that shows, for each product category, the total number of orders and the average order amount. Which BigQuery SQL clause or function is essential for grouping the data by product category?

   • AORDER BY
   • BJOIN
   • CHAVING
   • DGROUP BY
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2. 7expert · medium

   A ride-sharing company wants to reduce driver wait times. They have a BigQuery table with ride requests (request_time, pickup_location, driver_assignment_time). The data shows that wait times are higher during certain hours. An analyst needs to identify the peak demand hours and present the findings to operations. Which approach should they take?

   • ACalculate the average wait time per hour of the day and plot it as a line chart, then highlight the hours with the highest averages.
   • BCalculate the total number of ride requests per hour and plot it as a bar chart, then identify the hours with the most requests.
   • CCalculate the median wait time per hour and plot it as a box plot, then identify the hours with the highest median.
   • DCalculate the 90th percentile of wait time per hour and plot it as a bar chart, then identify the hours with the highest 90th percentile.
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4. 8foundation · easy

   A business stakeholder asks: 'Which product categories have seen the highest growth in sales over the last quarter?' What is the first step in translating this business question into an analytical workflow?

   • AAsk the stakeholder to provide the data.
   • BImmediately write a SQL query to get all sales data.
   • CDefine the metrics and dimensions needed, such as sales amount and category, and the time period.
   • DCreate a visualization of all sales data.
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5. 9expert · medium

   A data analyst is creating a report for executives on sales performance. They have a pandas DataFrame with monthly sales data for multiple regions. They want to create a chart that shows the trend for each region over time, but the chart must be easy to read with multiple regions. Which visualization should they use?

   • AA stacked bar chart with one bar per month, segmented by region.
   • BA pie chart showing the total sales per region for the entire period.
   • CA scatter plot with month on the x-axis and sales on the y-axis, with points colored by region.
   • DA line chart with one line per region, using different colors and a legend.
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6. 10foundation · easy

   You have a pandas DataFrame `df` with columns 'date' and 'sales' in a Jupyter notebook. Which matplotlib code creates a line plot of sales over time?

   • Adf.hist(column='sales')
   • Bdf.plot(kind='bar', x='date', y='sales')
   • Cplt.scatter(df['date'], df['sales'])
   • Ddf.plot(x='date', y='sales')
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