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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing and Analytics	20-30%	- Aggregate and summarize data - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Query and analyze datasets - Build and maintain data pipelines
Topic 2: Data Preparation and Exploration	20-30%	- Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Identify data quality issues - Ingest and acquire data - Transform and prepare data for analysis
Topic 3: Data Visualization and Insights	20-30%	- Create dashboards and reports - Build visualizations using Looker Studio - Interpret and communicate findings - Choose appropriate visualization types - Present data insights to stakeholders
Topic 4: Data-Driven Decision Making	10-20%	- Assess data quality and completeness - Define success metrics - Translate business requirements into data solutions - Identify stakeholders and requirements

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Google Associate Data Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
B. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
C. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
D. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

Answer: C

NEW QUESTION # 64
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Evaluate data drift.
B. Evaluate the data skewness.
C. Compare the two different models.
D. Compare the confusion matrix.

Answer: A

NEW QUESTION # 65
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B. Use Dataform to define the transformations in SQLX.
C. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Answer: B

NEW QUESTION # 66
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 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: B

NEW QUESTION # 67
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 cross join across all of the regional sales tables. Use the row_number() 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 union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Answer: D

NEW QUESTION # 68
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