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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q29-Q34):

NEW QUESTION # 29
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 Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
B. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
C. 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.
D. 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.

Answer: A

NEW QUESTION # 30
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_uype='logisric_reg') AS SELECT * from cusromer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_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 # 31
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. Store all data in a single BigQuery table without partitioning or lifecycle policies.
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. 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.
D. 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.

Answer: C

NEW QUESTION # 32
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
B. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
C. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Answer: A

NEW QUESTION # 33
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A. Require users to specify a partition filter using the alter table statement in SQL.
B. Create a view that filters out rows that are older than one year.
C. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
D. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

Answer: C

NEW QUESTION # 34
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