GOOGLE ASSOCIATE-DATA-PRACTITIONER LATEST BRAINDUMPS EBOOK, LATEST ASSOCIATE-DATA-PRACTITIONER EXAM QUESTIONS

Google Associate-Data-Practitioner Latest Braindumps Ebook, Latest Associate-Data-Practitioner Exam Questions

Google Associate-Data-Practitioner Latest Braindumps Ebook, Latest Associate-Data-Practitioner Exam Questions

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The Google Cloud Associate Data Practitioner Associate-Data-Practitioner practice test is available in three compatible and user-friendly formats. These formats are Associate-Data-Practitioner desktop practice test software, Google Cloud Associate Data Practitioner Associate-Data-Practitioner web-based practice exam, and Google Associate-Data-Practitioner PDF dumps file. All three formats of Associate-Data-Practitioner study material contain actual and verified Google Cloud Associate Data Practitioner Associate-Data-Practitioner Exam Dumps that will help you boost your exam preparation. The Google desktop practice test software and web-based Associate-Data-Practitioner practice test both simulate the actual exam environment and identify your mistakes.

Google Cloud Associate Data Practitioner Sample Questions (Q101-Q106):

NEW QUESTION # 101
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

  • A. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
  • B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
  • C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
  • D. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Analytics Hub allows you to share datasets with external partners while maintaining control over access.
Subscriptions allow granular control.
Why other options are incorrect:B: Cloud storage is for files, not bigquery datasets.
C: IAM roles do not allow for granular row and column level control.
D: Creating a separate project for each partner is complex and not scalable.


NEW QUESTION # 102
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. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
  • B. 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.
  • C. 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.
  • D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Answer: D

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 # 103
Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy-to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team-specific dashboard. What should you do?

  • A. 1. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 2. Create five subfolders inside the Shared folder. Grant each group the View access level to their corresponding subfolder.
  • B. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.
  • C. 1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3.
    Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.
  • D. 1. Move all team-specific content into the dashboard owner s personal folder. 2. Change the access level of the Shared folder to View for the All Users group. 3. Instruct each user to create content for their team in the user's personal folder.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Setting the Shared folder to "View" ensures everyone can see the content.
Creating Looker groups simplifies access management.
Subfolders allow granular permissions for each team.
Granting "Manage Access, Edit" allows teams to modify only their own content.
Why other options are incorrect:A: Grants View access only, so teams can't edit.
B: Moving content to personal folders defeats the purpose of sharing.
D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.


NEW QUESTION # 104
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

  • A. Grant the appropriate 1AM permissions on the dataset.
  • B. Create a data masking rule.
  • C. Create a row-level access policy.
  • D. Add a policy tag in BigQuery.

Answer: C

Explanation:
Creating arow-level access policyin BigQuery ensures that each sales representative can see only the transactions relevant to their region. Row-level access policies allow you to define fine-grained access control by filtering rows based on specific conditions, such as matching the sales representative's region. This approach enforces security while providing tailored data access, aligning with the principle of least privilege.
Extract from Google Documentation: From "Row-Level Security in BigQuery" (https://cloud.google.com
/bigquery/docs/row-level-security):"Row-level access policies let you restrict access to specific rows in a table based on a filter condition, such as a user's region, providing fine-grained control over data visibility without creating separate tables or views."


NEW QUESTION # 105
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

  • A. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
  • B. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
  • C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
  • D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

Answer: C

Explanation:
Using a Cloud Run function triggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.


NEW QUESTION # 106
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