Question 1

You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

AUse Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

BSchedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.

CUse Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

DCreate a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

Answer : C

Using Dataflow to implement a streaming pipeline triggered by an OBJECT_FINALIZE notification from Pub/Sub is the best solution. This approach automatically starts the data processing as soon as new files are uploaded to Cloud Storage, ensuring low latency. Dataflow can handle the data cleaning, deduplication, and enrichment with product information from the BigQuery table in a scalable and efficient manner. This solution minimizes overhead, as Dataflow is a fully managed service, and it is well-suited for real-time or near-real-time data pipelines.

Question 2

You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

AExport the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

BCopy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

CReference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

DReference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Answer : B

Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.

Question 3

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

AUse a Transfer Appliance.

BUse Cloud Storage FUSE.

CUse Storage Transfer Service.

DUse gcloud storage commands.

Answer : C

Using the Storage Transfer Service is the best solution for migrating data from an on-premises NFS to Google Cloud. This service allows you to control bandwidth usage by configuring transfer speed limits and provides detailed reporting on the migration status. Storage Transfer Service is specifically designed for large-scale data migrations and supports scheduling, monitoring, and error handling, making it an efficient and reliable choice for your use case.

Question 4

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?

ACreate a new field in the LookML layer, refresh your report, and select your new field from the field picker.

BCreate a calculated field using the Add a field option in Looker Studio, and add it to your report.

CCreate a table calculation from the field picker in Looker, and add it to your report.

DCreate a custom field from the field picker in Looker, and add it to your report.

Answer : D

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.

Question 5

Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

ACreate a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

BCreate an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

CCreate a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

DCreate a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

Answer : A

Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.

Free Practice Questions for Google Associate Data Practitioner Exam

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