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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage
Topic 2: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Topic 3: 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 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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Google Associate Data Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
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. 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.
D. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Answer: D

NEW QUESTION # 66
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 gcloud storage commands.
C. Use Cloud Storage FUSE.
D. Use Storage Transfer Service.

Answer: D

NEW QUESTION # 67
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. 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.
C. 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.
D. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Answer: A

NEW QUESTION # 68
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

A. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
B. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
C. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
D. Grant the partners the bigquery.user IAM role on the BigQuery project.

Answer: C

NEW QUESTION # 69
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Colab Enterprise to develop a custom model for purchase prediction.
B. Use BigQuery ML to create a logistic regression model for purchase prediction.
C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
D. Use Vertex Al Workbench to develop a custom model for purchase prediction.

Answer: B

NEW QUESTION # 70
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