Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?
A)
B)
C)
D)
Answer:B
Question — (Single Choice)
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?
A)
B)
C)
D)
Answer:C
Question — (Single Choice)
Your company is building a near real - time streaming pipeline to process JSON telemetry data from
small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the
serial number field, and write results to BigQuery. You want to use a managed service and write a
minimal amount of code for underlying transformations. What should you do?
Answer:C
Question — (Single Choice)
You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for
downstream reporting. You need to quickly build a scalable data pipeline that transforms the data
while providing insights into data quality issues. What should you do?
Answer:A
Question — (Single Choice)
You manage a Cloud Storage bucket that stores temporary files created during data processing. These
temporary files are only needed for seven days, after which they are no longer needed. To reduce
storage costs and keep your bucket organized, you want to automatically delete these files once they
are older than seven days. What should you do?
Answer:B
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