Google Associate Data Practitioner - ADP certification

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

• A. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
• B. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
• C. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
• D. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

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?

• A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• B. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
• D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

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?

• A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
• B. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
• C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
• D. Apply a filter to only show products with a positive profit margin.

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.

Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

• A. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
• B. Create a multi-region bucket, and upload the files to this bucket.
• C. Create a dual-region bucket, and upload the files to this bucket.
• D. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.