Practice Exams | Google Cloud Professional Data Engineer

Are you gearing up for the Google Professional Cloud Data Engineer certification exam? Welcome to the ideal place to evaluate your preparedness through our specially designed practice exams.

Our tests assess your proficiency in building scalable and highly available applications utilizing Google's robust tools and adhering to the industry's best practices.

Succeeding in this exam will spotlight your expertise in cloud-native applications. It will showcase your ability to employ the right tools for development, leverage managed services, and utilize cutting-edge databases.

Why is this significant? Because it amplifies your career prospects. Professionals with these skills are in high demand in today's industry.

In this course, we present a collection of practice tests comprising both essential knowledge questions that every cloud development professional must know, and specialized queries. Here's what we offer:

• 302 unique, high-quality test questions.

• Detailed explanations for both correct and incorrect answers.

• Insights into best practices, enriched with references to official guidelines.

• Importantly, our materials do not include outdated 'Case Studies' questions, which have been officially excluded from the exam by Google.

Our content is carefully curated to enhance your understanding and prime you for success with our well-constructed materials.

So, don't hesitate. Embark on this journey and put your knowledge to the test with our practice exams!

Quality speaks for itself..

SAMPLE QUESTION:

An external customer provides you with a daily data dump from their database, which arrives in Google Cloud Storage (GCS) as comma-separated values (CSV) files. You aim to analyze this data using Google BigQuery.

However, the data may contain rows that are incorrectly formatted or corrupted. How should you construct this pipeline?

A. Use federated data sources and validate the data within the SQL query.

B. Enable BigQuery monitoring in Google Cloud Operations Suite(updated Stackdriver) and set up an alert.

C. Import the data into BigQuery using the gcloud CLI, setting max_bad_records to 0.

D. Implement a Google Cloud Dataflow batch pipeline to load the data into BigQuery, directing errors to a separate dead-letter table for analysis.

What's your guess? Scroll below for the answer..

Explanation

Incorrect Answers:

A. Use federated data sources and validate the data within the SQL query.
While federated queries enable BigQuery to directly query data stored in external sources like GCS, they are not optimized for extensive data validation. Performing complex validation within SQL queries can lead to performance bottlenecks and may not efficiently handle corrupted data.

B. Enable BigQuery monitoring in Google Cloud Operations Suite(updated Stackdriver) and set up an alert.
Monitoring tools like Cloud Operations Suite(updated Stackdriver) are designed to track system performance and alert on specific metrics. However, they do not provide mechanisms for data validation or correction within the data pipeline.

C. Import the data into BigQuery using the gcloud CLI, setting max_bad_records to 0.

Setting max_bad_records to 0 enforces strict data integrity by rejecting any files containing errors. This strictness can cause the entire data load to fail upon encountering a single bad record, leading to potential disruptions in data availability.

Correct answer:

D. Implement a Google Cloud Dataflow batch pipeline to load the data into BigQuery, directing errors to a separate dead-letter table for analysis.

Utilizing Google Cloud Dataflow allows for the creation of a robust data processing pipeline capable of handling and transforming data before loading it into BigQuery. By configuring the pipeline to route erroneous records to a dead-letter table, you can effectively manage and inspect problematic data without interrupting the main data flow. This approach ensures that only clean, correctly formatted data is loaded into BigQuery, enhancing the reliability and accuracy of your analyses.

Links:

Links to Documentation

Welcome to the best practice exams to help you prepare for your Google Professional Cloud Data Engineer  exam.

• You can retake the exams as many times as you want

• This is a huge original question bank

• You get support from instructor if you have questions

• Each question has a detailed explanation

• Mobile-compatible with the Udemy app

• 30-days money-back guarantee if you're not satisfied

We hope that by now you're convinced! And there are a lot more questions inside the course.

Happy learning and best of luck for yourGoogle Professional Cloud Data Engineer exam!