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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data-Driven Decision Making | 10-20% | - Translate business requirements into data solutions - Define success metrics - Assess data quality and completeness - Identify stakeholders and requirements |
| Topic 2: Data Preparation and Exploration | 20-30% | - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis - Ingest and acquire data - Explore data through visualization and queries - Identify data quality issues |
| Topic 3: Data Visualization and Insights | 20-30% | - Interpret and communicate findings - Build visualizations using Looker Studio - Create dashboards and reports - Present data insights to stakeholders - Choose appropriate visualization types |
| Topic 4: Data Processing and Analytics | 20-30% | - Aggregate and summarize data - Build and maintain data pipelines - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Query and analyze datasets |
Google Associate Data Practitioner Sample Questions:
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. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- B. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- C. 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.
- D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
Correct Answer: C 🗳️
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
Correct Answer: C 🗳️
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. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- B. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- C. 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.
- D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
Correct Answer: C 🗳️
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. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
- B. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
- C. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
- D. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
Correct Answer: B 🗳️
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
- A. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
- B. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
- C. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
- D. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
Correct Answer: C 🗳️
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