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Google Associate Data Practitioner Sample Questions (Q14-Q19):

NEW QUESTION # 14
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 Dataform to define the transformations in SQLX.
B. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
C. Create BigQuery scheduled queries to define the transformations in SQL.
D. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

Answer: A

NEW QUESTION # 15
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
B. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Answer: D

NEW QUESTION # 16
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 Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: B

NEW QUESTION # 17
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C. Use Cloud Scheduler to schedule the jobs to run.
D. Use Cloud Tasks to schedule and run the jobs asynchronously.

Answer: A

NEW QUESTION # 18
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 (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_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_uype='logisric_reg') AS SELECT * from cusromer_data;

Answer: B

NEW QUESTION # 19
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Pass Guaranteed Quiz 2026 Trustable Google ADP Practice Test

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Valid ADP Test Forum, ADP New Dumps Free

Google Associate Data Practitioner Sample Questions (Q14-Q19):

Professional ADP Practice Test - 100% Pass ADP Exam

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