2026 ADP Study Guide | Useful Associate Data Practitioner 100% Free Reliable Dumps Questions

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Storage solutions selection 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal - Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion
Topic 2: Data Pipeline Orchestration	18%	- Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Pipeline automation and scheduling 1. Schedule and monitor data jobs 2. Error handling and monitoring - Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes
Topic 3: Data Management and Governance	25%	- Data quality and maintenance 1. Data validation and cleaning 2. Data lifecycle management - Compliance and governance 1. Data stewardship and cataloging 2. Data privacy and regulatory requirements - Data security and access control 1. Data encryption and protection 2. IAM roles and permissions
Topic 4: Data Analysis and Presentation	27%	- Data exploration and analysis 1. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis - Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively - Data visualization and reporting 1. Visualization best practices 2. Looker dashboards and reports

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Reliable ADP Dumps Questions, Free ADP Practice Exams

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Google Associate Data Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
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. 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.
B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
C. Require users to specify a partition filter using the alter table statement in SQL.
D. Create a view that filters out rows that are older than one year.

Answer: B

NEW QUESTION # 44
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use a Transfer Appliance.
B. Use Storage Transfer Service.
C. Use gcloud storage commands.
D. Use Cloud Storage FUSE.

Answer: B

NEW QUESTION # 45
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C. Create a Cloud Run function that runs daily and deletes files older than seven days.
D. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Answer: B

NEW QUESTION # 46
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

Answer: C

NEW QUESTION # 47
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. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
B. 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.
C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

Answer: D

NEW QUESTION # 48
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