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Google ADP Exam Syllabus Topics:

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

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Exam ADP Vce | Exam ADP Score

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Google Associate Data Practitioner Sample Questions (Q23-Q28):

NEW QUESTION # 23
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A. Use Cloud Run functions to transform and load the data into BigQuery.
B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
C. Use Dataflow to transform the data and write the transformed data to BigQuery.
D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Answer: C

NEW QUESTION # 24
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(model_type='logistic_reg*) as select ' except(churned) 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 (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

Answer: D

NEW QUESTION # 25
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

Answer: C

NEW QUESTION # 26
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 BigQuery subscription in Pub/Sub.
B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 27
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

NEW QUESTION # 28
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