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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

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Google Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, as well as scenario-based questions that require the candidate to demonstrate their problem-solving skills. Professional-Data-Engineer Exam is three hours long, and candidates must score at least 70% to pass. Professional-Data-Engineer exam fee is $200, and it is available in English, Japanese, and Spanish.

Google Certified Professional Data Engineer Exam Sample Questions (Q374-Q379):

NEW QUESTION # 374
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

A. VPN connection
B. SSH tunnel
C. FTP connection
D. Special browser

Answer: B

Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.

NEW QUESTION # 375
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?

A. Convert your batch BQ queries into interactive BQ queries.
B. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
C. Create an additional project to overcome the 2K on-demand per-project quota.
D. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.

Answer: D

NEW QUESTION # 376
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

A. Run a scheduled query to pull the necessary data at specific intervals daily.
B. Limit the query columns being pulled in the final result.
C. Use a cached query to accelerate time to results.
D. Create a materialized view based off of the query being run.

Answer: D

NEW QUESTION # 377
Why do you need to split a machine learning dataset into training data and test data?

A. To allow you to create unit tests in your code
B. So you can try two different sets of features
C. So you can use one dataset for a wide model and one for a deep model
D. To make sure your model is generalized for more than just the training data

Answer: D

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference:
https://machinelearningmastery.com/a-simple-intuition-for-overfitting/

NEW QUESTION # 378
You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Create a BigQuery table snapshot on a daily basis.
B. Access historical data by using time travel in BigQuery.
C. Migrate your data to multi-region BigQuery buckets.
D. Export the data from BigQuery into a new table that excludes the corrupted data

Answer: B

NEW QUESTION # 379
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