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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Processing and Analytics	20-30%	- Aggregate and summarize data - Query and analyze datasets - Build and maintain data pipelines - Use BigQuery and SQL for analytics - Apply statistical methods for analysis
Data-Driven Decision Making	10-20%	- Assess data quality and completeness - Translate business requirements into data solutions - Define success metrics - Identify stakeholders and requirements
Data Visualization and Insights	20-30%	- Present data insights to stakeholders - Interpret and communicate findings - Create dashboards and reports - Build visualizations using Looker Studio - Choose appropriate visualization types
Data Preparation and Exploration	20-30%	- Explore data through visualization and queries - Perform exploratory data analysis (EDA) - Identify data quality issues - Transform and prepare data for analysis - Ingest and acquire data

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Google Associate Data Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
B. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
C. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
D. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.

Answer: A

NEW QUESTION # 23
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. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

Answer: A

NEW QUESTION # 24
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
B. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
C. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Answer: C

NEW QUESTION # 25
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
D. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Answer: D

NEW QUESTION # 26
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. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
C. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
D. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

Answer: A

NEW QUESTION # 27
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