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

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Google Associate Data Practitioner Sample Questions (Q39-Q44):

NEW QUESTION # 39
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A. Cloud Storage CloudSQL for PostgreSQL Bigtable
B. Filestore Bigtable BigQuery
C. Cloud Storage BigQuery Firestore
D. Filestore Cloud SQL for PostgreSQL Datastore

Answer: C

NEW QUESTION # 40
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
B. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
C. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
D. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

Answer: C

NEW QUESTION # 41
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use BigQuery ML to create a logistic regression model for purchase prediction.
B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
D. Use Colab Enterprise to develop a custom model for purchase prediction.

Answer: A

NEW QUESTION # 42
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 # 43
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;

Answer: A

NEW QUESTION # 44
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