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Google GCP-ADP Sample Questions:
01. Which Google Cloud solutions can be used to clean and transform raw data before loading it into BigQuery?
(Choose three)
a) Cloud Data Fusion
b) Cloud Dataflow
c) BigQuery SQL
d) Cloud Spanner
02. 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 new field in the LookML layer, refresh your report, and select your new field from the field picker.
b) Create a table calculation from the field picker in Looker, and add it to your report.
c) Create a custom field from the field picker in Looker, and add it to your report.
d) Create a calculated field using the Add a field option in Looker Studio, and add it to your report.
03. 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?
a) Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
b) Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
c) Reference 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.
d) Reference 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.
04. You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders.
You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
a) Use Cloud Composer to orchestrate the Spark job and email the report.
b) Use Cloud Run functions to trigger the Spark job and email the report.
c) Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
d) Use Cloud Scheduler to trigger the Spark job, and use Cloud Run functions to email the report.
05. A company has terabytes of structured data stored in CSV files. What is the best approach to efficiently load and query this data in BigQuery?
(Choose three)
a) Load CSV files into Cloud Storage and use BigQuery External Tables
b) Convert CSV to Parquet before loading into BigQuery
c) Load CSV data directly into BigQuery
d) Store the CSV files in Firestore
06. You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of data. What should you do?
a) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
b) Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
c) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
d) Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
07. What are key benefits of Cloud Scheduler for managing data pipelines?
(Choose three)
a) It triggers jobs based on time schedules
b) It integrates with Pub/Sub, Cloud Functions, and HTTP endpoints
c) It replaces Cloud Composer for complex orchestration
d) It automates repetitive tasks
08. You have a logistics and shipping analytics solution that uses BigQuery as its data warehouse. You want to enhance the solution with real-time location data from your truck fleet and hourly traffic data.
You want to use the same data ingestion process for both real-time and batch data. What should you do?
a) Use Dataflow.
b) Use Cloud Data Fusion.
c) Use Storage Transfer Service.
d) Use BigQuery Data Transfer Service.
09. You created a new dashboard in Looker that displays live, up-to-the-minute sales data for your company. You want to provide access to your new dashboard to your organization’s sales team so they can see live data whenever needed. What should you do?
a) Use the Add Filter modal and add a user attribute filter to the dashboard based on the users’ email attribute.
b) Use the Manage Access panel for the folder where the dashboard is saved, and grant view access to the sales user group.
c) Use the Dashboard actions menu at the top of the dashboard to get the link to the dashboard. Send the dashboard’s URL to the sales team members.
d) Use the Dashboard actions menu at the top of the dashboard, and schedule a daily dashboard delivery to the sales team members’ email addresses.
10. Your retail company has an on-premises MySQL database that contains millions of rows of customer data. You need to efficiently migrate the data, in daily increments, into BigQuery for analysis. What should you do?
a) Export the data from MySQL in CSV format and upload the CSV file to Cloud Storage. Use BigQuery Data Transfer Service to load the data into BigQuery.
b) Use Cloud Data Fusion to connect to the MySQL database, create a pipeline to extract the data, and load the data into BigQuery.
c) Use Database Migration Service to replicate the MySQL database to Cloud SQL for MySQL. Use BigQuery Data Transfer Service to load the data into BigQuery.
d) Use Dataflow to connect to the MySQL database, extract the data, and load the data into BigQuery.
Answers:
|
Question: 01 Answer: a, b, c |
Question: 02 Answer: c |
Question: 03 Answer: d |
Question: 04 Answer: c |
Question: 05 Answer: a, b, c |
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Question: 06 Answer: a |
Question: 07 Answer: a, b, d |
Question: 08 Answer: a |
Question: 09 Answer: b |
Question: 10 Answer: c |
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