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 dat a. What should you do?

• A.Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.
• B.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.
• C.Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.
• D.Use BigQuery's built-in functions to perform data quality transformations.

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You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

• A.Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
• B.Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
• C.Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
• D.Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

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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.Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
• B.Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
• 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.

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You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

• A.Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
• B.Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
• C.Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
• D.Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.

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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.Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
• B.Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
• C.Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
• D.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.

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