Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully -managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team’s Python skills. What should you do?
You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?
You need to transfer approximately 300 TB of data from your company's on -premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?
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?
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement, and previous click behavior. After training the model, you want to generate predictions on new dat a. Which model type should you use in BigQuery ML?
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?
