2.1 Identify Data Trends, Patterns, and Insights by Using BigQuery and Jupyter Notebooks ASSOCIATE-DATA-PRACTITIONER Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   You want to count the number of orders in a BigQuery table named 'orders'. Which SQL query returns the total number of rows in the table?

   • ASELECT SUM(*) FROM orders;
   • BSELECT NUM_ROWS FROM orders;
   • CSELECT TOTAL(*) FROM orders;
   • DSELECT COUNT(*) FROM orders;
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2. 2foundation · easy

   You are using a Jupyter notebook in Google Cloud's Colab Enterprise to analyze data stored in BigQuery. What is the primary way to run a SQL query against BigQuery from within a notebook cell?

   • AUse the `bq` command-line tool directly in a notebook cell.
   • BUse the `%%sql` magic command with the BigQuery engine.
   • CUse the BigQuery client library in a Python cell, such as `client.query('SELECT ...')`.
   • DUse the `gcloud` command in a notebook cell.
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4. 3foundation · easy

   You want to create a bar chart in a Jupyter notebook using seaborn to show the average sales per product category. Which seaborn function is appropriate?

   • Asns.scatterplot(data=df, x='category', y='sales')
   • Bsns.histplot(data=df, x='sales')
   • Csns.lineplot(data=df, x='category', y='sales')
   • Dsns.barplot(data=df, x='category', y='sales')
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5. 4application · medium

   A data analyst is using a Jupyter notebook to analyze data stored in BigQuery. They want to ensure that the notebook can be shared with colleagues who have different Google Cloud projects. Which approach should they take?

   • AAsk colleagues to modify the notebook to use their own project ID before running it.
   • BUse the %%bigquery magic command without specifying a project ID, and rely on the default project of the notebook environment.
   • CUse a service account key file embedded in the notebook to authenticate to BigQuery.
   • DHardcode the project ID in the query strings so that the notebook always queries the correct project.
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6. 5foundation · easy

   After running a query to analyze customer churn, you find that the churn rate is higher than expected. What is the most appropriate next step in the business question analysis workflow?

   • AImmediately present the raw numbers to stakeholders.
   • BDelete the query and start over.
   • CInterpret the results in the context of the business question and consider additional factors or data.
   • DAssume the data is incorrect.
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