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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Explore data through visualization and queries - Identify data quality issues - Ingest and acquire data - Perform exploratory data analysis (EDA) |
| Data Processing and Analytics | 20-30% | - Query and analyze datasets - Use BigQuery and SQL for analytics - Apply statistical methods for analysis - Build and maintain data pipelines - Aggregate and summarize data |
| Data-Driven Decision Making | 10-20% | - Translate business requirements into data solutions - Define success metrics - Assess data quality and completeness - Identify stakeholders and requirements |
| Data Visualization and Insights | 20-30% | - Create dashboards and reports - Choose appropriate visualization types - Present data insights to stakeholders - Interpret and communicate findings - Build visualizations using Looker Studio |
Google Associate Data Practitioner Sample Questions:
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. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
- B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
- C. 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.
- D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
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 a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data 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 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.
- D. 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.
Correct Answer: A 🗳️
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. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
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 🗳️
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