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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q21-Q26):

NEW QUESTION # 21
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
C. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
D. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

Answer: B

NEW QUESTION # 22
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
B. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
C. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
D. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

Answer: C

NEW QUESTION # 23
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. Compare the confusion matrix.
C. Evaluate the data skewness.
D. Compare the two different models.

Answer: A

NEW QUESTION # 24
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. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
B. 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.
C. 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.
D. 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.

Answer: A

NEW QUESTION # 25
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
B. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.

Answer: D

NEW QUESTION # 26
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