Google Cloud Data engineer free practice test.

This practice test provides targeted preparation for aspiring Data engineers on the Google Cloud platform. It covers essential skills such as identity management and creates and manages robust data processing systems. This includes the ability to design, build, deploy, monitor, maintain, and secure data processing workloads. This resource aids individuals in assessing their readiness and refining their expertise in data engineering practices.

Welcome to your GCP professional data engineer

When designing your Cloud Bigtable schema which is a recommended design best practice to help avoid slow query performance?

You're running a Dataflow pipeline that processes streaming data from Pub/Sub and writes the processed data to BigQuery. You notice that due to a bug in the pipeline's code, some of the data written to BigQuery in the last 24 hours is corrupted. How can you recover the corrupted data?

A solar company is using Dataproc to process a large number of CSV files. The storage option they choose needs to be flexible to serve many worker nodes in multiple clusters. These worker nodes will read the data and also write to it for intermediate storage between processing jobs. What is the recommended storage option on Google Cloud?

A global logistics company is looking to optimize its data analytics capabilities by designing a new data model for its burgeoning data lake on GCP. This data lake will centralize diverse datasets, including customer transactions, product inventories, supply chain logistics, and online user interactions. The goal is to enable advanced analytics, machine learning models for demand forecasting, and real-time inventory management. The company is particularly interested in a cost-efficient design that scales with their data growth while ensuring high query performance. Which of the following data model designs for the GCP Data Lake maximizes cost efficiency while maintaining optimal query performance for the company's analytical needs?

A Gaming company is deploying a new data analytics platform on GCP and requires a robust data governance framework to manage data staging, cataloging, and discovery. The platform must efficiently handle diverse data types and sources, ensuring data quality and compliance. What architectural approach should be taken to meet these requirements?

A developer in your team is testing a new application that makes frequent calls to a GCP service's API. They start receiving "rate limit exceeded" errors. What would be the best approach to troubleshoot and resolve this issue? Select two choices.

A media company is migrating its private data centers to Google Cloud. Over many years, hundreds of terabytes of data were accumulated. They currently have a 100 Mbps line and they need to transfer this data reliably before commencing operations on Google Cloud in 45 days. What should they do?

Your organization's data and applications reside in multiple geographies on Google Cloud. Some regional laws require you to hold your own keys outside of the cloud provider environment, whereas other laws are less restrictive and allow storing keys with the same provider who stores the data. The management of these keys has increased in complexity, and you need a solution that can centrally manage all your keys. What should you do?

. Business analysts in your team need to run analysis on data that was loaded into BigQuery. You need to follow recommended practices and grant permissions. What role should you grant the business analysts?

A Financial institution is migrating its on-premises data to a data warehouse on Google Cloud. This data will be made available to business analysts. Local regulations require that customer information including credit card numbers, phone numbers, and email IDs be captured, but not used in analysis. They need to use a reliable, recommended solution to redact the sensitive data. What should they do?

You have a Dataflow pipeline that runs data processing jobs. You need to identify the parts of the pipeline code that consume the most resources. What should you do?

Your company is developing a data pipeline, which will be used as part of a broader ML pipeline for end-to-end machine learning lifecycle management. The data pipeline needs to be able to prepare data, transform data, analyze data, and load data to downstream systems like BigQuery, Cloud SQL. It is required to use Google Cloud for developing and deploying the pipeline. How do you develop the pipeline quickly with a no-code, low-code approach? (Choose Two)

Your company has a Big Query dataset named financial records with a table of quarterly earnings. You need to modify the column-level security policy to grant a new group, audit team, access to the net income column, which was previously restricted. Which of the following steps would you take to accomplish this?

You are deploying a cluster of Compute Engine instances that will process large datasets stored in Cloud Storage. To ensure the system is fault-tolerant and can handle instance failures without interrupting the data processing tasks, what feature or strategy should you implement?

A mining company data engineering team receives data in JSON format from external sources at the end of each day. You need to design the data pipeline. What should you do?

You are running Dataflow jobs for data processing. When developers update the code in Cloud Source Repositories, you need to test and deploy the updated code with minimal effort. Which of these would you use to build your continuous integration and delivery (CI/CD) pipeline for data processing?

You are creating a data pipeline for streaming data on Dataflow for a e-commerce site point of sales data. You want to calculate the total sales per hour on a continuous basis. Which of these windowing options should you use?

An ecommerce site is processing large amounts of input data in BigQuery. You need to combine this data with a small amount of frequently changing data that is available in Cloud SQL. What should you do?

As a data manager in your firm you manage a PySpark batch data pipeline by using Dataproc .You want to take a hands-off approach to running the workload, and you do not want to provision and manage your own cluster. What should you do?

You want to build a streaming data analytics pipeline in Google Cloud. You need to choose the right products that support streaming data. Which of these would you choose?

Also, check out the professional cloud security engineer free practice test

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