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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Topic 2: Data Management	25%	- Data governance and security 1. IAM and access control 2. Data lifecycle management - Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies
Topic 3: Data Analysis and Presentation	27%	- Querying and analyzing data 1. BigQuery SQL usage 2. Data aggregation and transformation - Data visualization 1. Looker / Looker Studio reporting
Topic 4: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts

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Free PDF Quiz 2026 Google ADP: Useful Associate Data Practitioner Reliable Torrent

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Google Associate Data Practitioner Sample Questions (Q31-Q36):

NEW QUESTION # 31
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
B. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
C. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
D. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer: B

NEW QUESTION # 32
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. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
B. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
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 # 33
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. 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.
B. Store all data in a single BigQuery table without partitioning or lifecycle policies.
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. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: C

NEW QUESTION # 34
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
B. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Answer: D

NEW QUESTION # 35
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 Dataflow to transform the data and write the transformed data to BigQuery.
B. Use Cloud Run functions to transform and load the data into BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

Answer: A

NEW QUESTION # 36
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