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Google Associate Data Practitioner Sample Questions:

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

2. Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A) Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
B) Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
C) Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
D) Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

3. Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
B) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
C) Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

4. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

5. Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B) Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
C) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D) Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

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