Associate-Data-Practitioner Google Cloud Associate Data Practitioner (ADP Exam) exact Exam Questions

Google Cloud Associate Data Practitioner (ADP Exam)

Last Update 15 hours ago Total Questions : 106

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Question # 11

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?

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.

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.

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.

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.

Question # 12

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

Create a partition by transaction date, and set the partition expiration policy to seven years.

Set the table-level retention policy in BigQuery to seven years.

Set the dataset-level retention policy in BigQuery to seven years.

Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

Question # 13

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Apply a filter to only show products with a positive profit margin.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Question # 14

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company’s products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Apply a filter to only show products with a positive profit margin.

Question # 15

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

Create a sales_region user attribute, and assign each manager’s region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.

Create five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

Question # 16

Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.

Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.

Question # 17

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

Question # 18

You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Question # 19

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

Set up a Cloud CDN in front of the bucket.

Enable Object Versioning on the bucket.

Store the data in a multi-region bucket.

Store the data in Nearline storaqe.

Question # 20

You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the data. What should you do?

Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.

Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.

Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.

Use BigQuery's built-in functions to perform data quality transformations.