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The TorrentValid offers three formats of study materials for the Associate Data Practitioner (ADP) certification exam preparation. Our product is designed by experts in their respective fields, ensuring that our customers receive the most up-to-date and accurate Google ADP Exam Questions.
| Section | Weight | Objectives |
|---|
| Data Preparation and Ingestion | 30% | - Data ingestion into Google Cloud services
- 1. Cloud Storage ingestion patterns
- 2. Streaming ingestion with Pub/Sub concepts
|
| Data Management | 25% | - Data governance and security
- 1. Data lifecycle management
- 2. IAM and access control
- Storage and data organization
- 1. BigQuery datasets and tables management
- 2. Cloud Storage lifecycle policies
|
| Data Analysis and Presentation | 27% | - Data visualization
- 1. Looker / Looker Studio reporting
- Querying and analyzing data
- 1. BigQuery SQL usage
- 2. Data aggregation and transformation
|
| Data Pipeline Orchestration | 18% | - Pipeline design and automation
- 1. Dataflow pipelines
- 2. Workflow orchestration concepts (e.g., Cloud Composer)
|
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Google Associate Data Practitioner Sample Questions (Q61-Q66):
NEW QUESTION # 61
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?
- A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
- B. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
- C. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
Answer: B
NEW QUESTION # 62
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. Store all data in a single BigQuery table without partitioning or lifecycle policies.
- B. 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.
- 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 # 63
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. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
- C. Use Dataflow to transform the data and write the transformed data to BigQuery.
- D. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
Answer: C
NEW QUESTION # 64
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 # 65
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 Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
Answer: D
NEW QUESTION # 66
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