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Your company wants to analyze sales data, which is growing at a rapid pace. The sales data is currently stored in Google Cloud Storage. The business wants the ability to perform ad-hoc analysis on the sales data, including querying specific data ranges, filtering by various dimensions, and performing aggregate functions. They also want to visualize their sales data with interactive dashboards and reports. Which combination of Google Cloud products would you recommend?
You are a data engineer working for a media company that is looking to implement a machine learning solution to automatically categorize news articles based on their content. You want to utilize Google Cloud's pre-built machine learning models due to their ease of use and you don't have the resources to train and maintain a custom model. Which service should you primarily use to achieve this task?
Your company needs to store and process large amounts of scientific data generated from simulations and experiments. The data needs to be stored in a scalable, secure, and cost-effective manner. What is the recommended approach for storing and processing this scientific data using Google Cloud services?
You are required to establish a connection to your Linux virtual machine in Google Cloud Platform (GCP). When do you not have the obligation to generate SSH keys on your own? Select all that apply.
Select all that apply
You are working as a data engineer and you have been asked to develop a real-time predictive Machine Learning pipeline in Google Cloud. Your company's data is streaming continuously and stored in Cloud Storage. The Machine Learning models must be retrained frequently with the most recent data and then deployed to Cloud AI Platform. Which of the following approaches would be most appropriate to achieve this?
A global media company has hired you to design a data pipeline that can process both batch and real-time data streams from different regions worldwide. The pipeline should be able to scale automatically based on the volume of data, support complex transformations, and make the data available in near real-time for a machine learning model hosted on AI Platform. The system should also be fault-tolerant to ensure data reliability. What would be your recommendation for such a system?
A marketing company wants to build a solution for processing large amounts of customer data to predict customer behavior. They want to be able to run complex machine learning algorithms on this data and scale their processing power as needed. What solution would you recommend for this use case to process large amounts of customer data, run complex machine learning algorithms, and scale processing power as needed?
As a data engineer, you have been asked to integrate data from a third-party vendor's RESTful API. The data from this API needs to be ingested in real-time, transformed, and then stored in BigQuery for real-time analysis. Which approach would you use to implement this?
You are working on a data engineering project where you need to ingest streaming data and perform real-time analysis. The data comes in high volumes, and the processing needs to scale based on the data volume. You have chosen to use Google Cloud Platform for this project. What should you do to meet these requirements?
You're a data engineer in a financial organization. The company has built a machine learning model for fraud detection, deployed on Google AI Platform. The model needs continuous evaluation since fraudulent patterns can evolve over time. The prediction input and output are saved in BigQuery. Which approach should you use for continuous evaluation?
