LATEST RELEASED GOOGLE FREE PROFESSIONAL-DATA-ENGINEER EXAM: GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM - PRACTICE TEST PROFESSIONAL-DATA-ENGINEER FEE

Latest Released Google Free Professional-Data-Engineer Exam: Google Certified Professional Data Engineer Exam - Practice Test Professional-Data-Engineer Fee

Latest Released Google Free Professional-Data-Engineer Exam: Google Certified Professional Data Engineer Exam - Practice Test Professional-Data-Engineer Fee

Blog Article

Tags: Free Professional-Data-Engineer Exam, Practice Test Professional-Data-Engineer Fee, Professional-Data-Engineer Valid Test Registration, Professional-Data-Engineer Passing Score Feedback, Exam Professional-Data-Engineer Consultant

BONUS!!! Download part of Dumps4PDF Professional-Data-Engineer dumps for free: https://drive.google.com/open?id=1W4DHG4Pkn1tLbTVPTNxV0MUk7jIeSjZh

In order to cater to different consumption needs for different customers, we have three versions for Professional-Data-Engineer exam brindumps, hence you can choose the version according to your own needs. Professional-Data-Engineer PDF version is printable, if you choose it you can take the paper one with you, and you can practice it anytime. Professional-Data-Engineer soft test engine can stimulate the test environment, and you will be familiar with the test environment by using it. Professional-Data-Engineer online test engine support all web browsers, and you can use this version in your phone.

Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions

The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.

Google Professional-Data-Engineer Certification Exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.

>> Free Professional-Data-Engineer Exam <<

Fantastic Google - Professional-Data-Engineer - Free Google Certified Professional Data Engineer Exam Exam

Our Professional-Data-Engineer exam questions are related to test standards and are made in the form of actual tests. Whether you are newbie or experienced exam candidates, our Professional-Data-Engineer study guide will relieve you of tremendous pressure and help you conquer the difficulties with efficiency. If you study with our Professional-Data-Engineer Practice Engine for 20 to 30 hours, we can claim that you can pass the exam as easy as a pie. Why not have a try?

Google Certified Professional Data Engineer Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

  • A. Use federated data sources, and check data in the SQL query.
  • B. Enable BigQuery monitoring in Google Stackdriver and create an alert.
  • C. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
  • D. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

Answer: D

Explanation:
Topic 1, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.


NEW QUESTION # 63
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?

  • A. Use Cloud TPUs without any additional adjustment to your code.
  • B. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
  • C. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
  • D. Stay on CPUs, and increase the size of the cluster you're training your model on.

Answer: B

Explanation:
Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.


NEW QUESTION # 64
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

  • A. Spark
  • B. Blaze
  • C. Fire
  • D. Ignite

Answer: A

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/


NEW QUESTION # 65
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Establish a Cloud Interconnect between all remote data centers and Google.
  • B. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • C. Deploy small Kafka clusters in your data centers to buffer events.
  • D. Have the data acquisition devices publish data to Cloud Pub/Sub.

Answer: D

Explanation:
Pubsub is global service with high message delivery capacity.


NEW QUESTION # 66
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?

  • A. Deploy the model and measure the real-world AUC; it's always higher because of generalization
  • B. Perform hyperparameter tuning
  • C. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
  • D. Train a classifier with deep neural networks, because neural networks would always beat SVMs

Answer: B


NEW QUESTION # 67
......

Our Professional-Data-Engineer exam questions are compiled by experts and approved by the professionals with years of experiences. The language is easy to be understood which makes any learners have no obstacles and our Professional-Data-Engineer guide torrent is suitable for anyone. The content is easy to be mastered and has simplified the important information. Our Professional-Data-Engineer test torrents convey more important information with less questions and answers and thus make the learning relaxing and efficient. With our Professional-Data-Engineer exam questions, your will pass the Professional-Data-Engineer exam with ease.

Practice Test Professional-Data-Engineer Fee: https://www.dumps4pdf.com/Professional-Data-Engineer-valid-braindumps.html

P.S. Free 2025 Google Professional-Data-Engineer dumps are available on Google Drive shared by Dumps4PDF: https://drive.google.com/open?id=1W4DHG4Pkn1tLbTVPTNxV0MUk7jIeSjZh

Report this page