Google Associate Data Practitioner Sample Questions:

1. 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) ETL
B) ETLT
C) ELT
D) EL

2. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Dataform workflows.
B) Use Cloud Composer operators.
C) Use Dataflow pipelines.
D) Use Cloud Data Fusion pipelines.

3. Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A) Store the data in Cloud Storage using Coldline storage.
B) Store the data in Cloud Storage using Archive storage.
C) Store the data in Cloud Storage using Nearline storage.
D) Store the data in Cloud Storage using Standard storage.

4. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
C) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

5. You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A) Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
B) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

Solutions: