Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

• A. Store the data in Cloud Storage using Nearline storage.
• B. Store the data in Cloud Storage using Standard storage.
• C. Store the data in Cloud Storage using Archive storage.
• D. Store the data in Cloud Storage using Coldline storage.

Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

• A. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
• B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
• C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
• D. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

• A. ETL
• B. EL
• C. ETLT
• D. ELT

Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

• A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
• B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
• C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
• D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

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. Apply a filter to only show products with a positive profit margin.
• 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. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.