PRACTICE GOOGLE ASSOCIATE-DATA-PRACTITIONER EXAM | ASSOCIATE-DATA-PRACTITIONER RELIABLE EXAM PAPERS

Practice Google Associate-Data-Practitioner Exam | Associate-Data-Practitioner Reliable Exam Papers

Practice Google Associate-Data-Practitioner Exam | Associate-Data-Practitioner Reliable Exam Papers

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Tags: Practice Associate-Data-Practitioner Exam, Associate-Data-Practitioner Reliable Exam Papers, Latest Associate-Data-Practitioner Exam Test, Dumps Associate-Data-Practitioner Free Download, Associate-Data-Practitioner Valid Braindumps Files

Getting the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) certification is the way to go if you're planning to get into Google or want to start earning money quickly. Success in the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) exam of this credential plays an essential role in the validation of your skills so that you can crack an interview or get a promotion in an Google company. Many people are attempting the Google Cloud Associate Data Practitioner (Associate-Data-Practitioner) test nowadays because its importance is growing rapidly. The product of Lead2Passed has many different premium features that help you use this product with ease. The study material has been made and updated after consulting with a lot of professionals and getting customers' reviews.

Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.
Topic 2
  • Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3
  • Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

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Associate-Data-Practitioner Reliable Exam Papers, Latest Associate-Data-Practitioner Exam Test

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Google Cloud Associate Data Practitioner Sample Questions (Q98-Q103):

NEW QUESTION # 98
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. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
  • B. 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.
  • 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: A

Explanation:
Setting the table partition expiration period to one year using the ALTER TABLE statement is the most efficient and cost-effective approach. This automatically deletes data in partitions older than one year, reducing storage costs without requiring manual intervention or additional queries. It minimizes administrative overhead and ensures compliance with your data retention policy while optimizing storage usage in BigQuery.


NEW QUESTION # 99
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 rowsand transactional data. 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. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
  • C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
  • D. 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.

Answer: B

Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.


NEW QUESTION # 100
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 data. You want to share the data while following Google-recommended practices. What should you do?

  • A. 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.
  • B. Grant the partners the bigquery.user IAM role on the BigQuery project.
  • C. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
  • D. 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.

Answer: A

Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google- recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.


NEW QUESTION # 101
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

  • A. Create a custom quota for each analyst in BigQuery.
  • B. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
  • C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
  • D. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.

Answer: B

Explanation:
Assigning each analyst to a separate project associated with their department and creating a single reservation for each department using BigQuery editions allows for precise cost management. By assigning each project to its department's reservation, you can allocate fixed compute resources and budgets for each department, ensuring that their query costs are predictable and controlled. This approach aligns with your organization's goal of creating a fixed budget for query costs while maintaining departmental separation and accountability.


NEW QUESTION # 102
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

  • A. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
  • B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
  • C. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.
  • D. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.

Answer: D

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:
* Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post- disaster. Primary operations remain in one zone, minimizing latency.
* Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour-too high for valuable data. Manual backup management adds overhead.
* Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.


NEW QUESTION # 103
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