Google Professional-Data-Engineer Practice Test Online, Latest Professional-Data-Engineer Exam Notes

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To become a Google Certified Professional Data Engineer, candidates need to pass the Professional-Data-Engineer exam. Professional-Data-Engineer exam is designed to test the knowledge and skills of professionals in designing, building, and maintaining data processing systems. Professional-Data-Engineer Exam consists of multiple-choice and scenario-based questions that test the candidate's ability to analyze and solve real-world problems related to data engineering.

Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
Your United States-based company has created an application for assessing and responding to user actions.
The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
* Single global endpoint
* ANSI SQL support
* Consistent access to the most up-to-date data
What should you do?

A. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.
B. Implement BigQuery with no region selected for storage or processing.
C. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
D. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

Answer: C

NEW QUESTION # 184
Case Study: 1 - Flowlogistic
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.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

A. Store the common data in BigQuery and expose authorized views.
B. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
C. Store the common data in BigQuery as partitioned tables.
D. Store the common data encoded as Avro in Google Cloud Storage.

Answer: A

NEW QUESTION # 185
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

A. Biases
B. Continuous features
C. Input values
D. Weights

Answer: A,D

Explanation:
A neural network is a simple mechanism that's implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.

NEW QUESTION # 186
You are building an application to share financial market data with consumers, who will receive data feeds.
Data is collected from the markets in real time. Consumers will receive the data in the following ways:
* Real-time event stream
* ANSI SQL access to real-time stream and historical data
* Batch historical exports
Which solution should you use?

A. Cloud Pub/Sub, Cloud Storage, BigQuery
B. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
C. Cloud Dataflow, Cloud SQL, Cloud Spanner
D. Cloud Dataproc, Cloud Dataflow, BigQuery

Answer: A

NEW QUESTION # 187
Does Dataflow process batch data pipelines or streaming data pipelines?

A. None of the above
B. Both Batch and Streaming Data Pipelines
C. Only Streaming Data Pipelines
D. Only Batch Data Pipelines

Answer: B

Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 188
......

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Biography

Google Professional-Data-Engineer Practice Test Online, Latest Professional-Data-Engineer Exam Notes

Latest Google Professional-Data-Engineer Exam Notes, Professional-Data-Engineer Reliable Real Test

Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

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Lee Reed Lee Reed

Biography

Google Professional-Data-Engineer Practice Test Online, Latest Professional-Data-Engineer Exam Notes

Latest Google Professional-Data-Engineer Exam Notes, Professional-Data-Engineer Reliable Real Test

Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

LIBERA TU POTENCIAL, CRECE MÁS ALLÁ DE LOS LÍMITES.

Necesitas ayuda?

Atajos

Postulate o Contactanos

Seguinos en

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