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Google Professional-Data-Engineer certification is a highly sought-after certification that is designed for data professionals who are interested in demonstrating their proficiency in designing, building, and managing data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for data engineers, data analysts, and data scientists who have experience working with data processing systems and are looking to advance their careers.
To pass the Google Professional-Data-Engineer exam, candidates must have a solid understanding of data engineering concepts and techniques, as well as practical experience working with the Google Cloud Platform. They must be able to design and implement data processing systems that are secure, scalable, and efficient, and have the ability to troubleshoot and optimize these systems as needed. Professional-Data-Engineer Exam is challenging and comprehensive, but passing it can open up many career opportunities in data engineering, especially for those interested in working with Google Cloud Platform.
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NEW QUESTION # 203
Which Java SDK class can you use to run your Dataflow programs locally?
Answer: A
Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests
NEW QUESTION # 204
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
Answer: C
NEW QUESTION # 205
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?
Answer: D
Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex
NEW QUESTION # 206
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
Answer: B
Explanation:
Topic 5, Practice Questions
NEW QUESTION # 207
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
Answer: B
NEW QUESTION # 208
......
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