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Google Associate Data Practitioner Sample Questions:
1. 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) CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
B) CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
C) CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
D) CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
2. You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
A) ELT
B) ETL
C) EL
D) ETLT
3. 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?
A) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
B) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
C) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D) Create a Cloud Run function that runs daily and deletes files older than seven days.
4. Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
A) Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B) Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
C) Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
D) Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
5. Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
A) Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
B) Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
C) Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
D) Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: A |
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