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【General】 Braindump Associate-Data-Practitioner Pdf, Examcollection Associate-Data-Practit

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Google Cloud Associate Data Practitioner Sample Questions (Q65-Q70):NEW QUESTION # 65
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?
  • A. Seek back to the last acknowledged message.
  • B. Forward unacknowledged messages to a dead-letter topic.
  • C. Retry messages until they are acknowledged.
    B Implement flow control on the subscribers
Answer: B
Explanation:
Implementingflow control on the subscribersallows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.

NEW QUESTION # 66
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. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
  • C. 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.
  • D. Grant the partners the bigquery.user IAM role on the BigQuery project.
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 # 67
You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?
  • A. Create a table calculation from the field picker in Looker, and add it to your report.
  • B. Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.
  • C. Create a calculated field using the Add a field option in Looker Studio, and add it to your report.
  • D. Create a custom field from the field picker in Looker, and add it to your report.
Answer: D
Explanation:
Creating a custom field from the field picker in Looker allows you to add new fields to your report without requiring the Develop permission. Custom fields are created directly in the Looker UI, enabling you to define calculations or transformations that generate SQL for the database query. This approach is user-friendly and does not require access to the LookML layer, making it the appropriate choice for your situation.

NEW QUESTION # 68
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. 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.
  • 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. 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.
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 # 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 Vertex AI Workbench to develop a custom model for purchase prediction.
  • B. Use Colab Enterprise to develop a custom model for purchase prediction.
  • C. Use BigQuery ML to create a logistic regression model for purchase prediction.
  • D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
Answer: C

NEW QUESTION # 70
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