Google ADP Exam : Associate Data Practitioner

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A) Apply a filter to only show products with a positive profit margin.
B) Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C) Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D) Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

2. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
C) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

3. 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_type='logistic_reg*) as select ' except(churned) FROM customer 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_uype='logisric_reg') AS SELECT * from cusromer_data;

4. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
B) Use Cloud Composer to orchestrate the Spark job and email the report.
C) Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
D) Use Cloud Run functions to trigger the Spark job and email the report.

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

Solutions: