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Die Google Professional-Data-Engineer-Prüfung ist eine Zertifizierung, die von Google für Fachleute angeboten wird, die sich auf Data Engineering spezialisiert haben. Diese Prüfung ist darauf ausgelegt, das Verständnis des Kandidaten für Datenverarbeitungssysteme, Datenmodellierung, Datenregierung und Datenumwandlung zu testen. Die Zertifizierung zielt darauf ab, die Expertise des Kandidaten in den Data Engineering-Technologien von Google Cloud Platform und deren Fähigkeit zur Gestaltung und Entwicklung effektiver Datenlösungen zu validieren.
Die Google Professional-Data-Engineer Zertifizierungsprüfung besteht aus Multiple-Choice- und Szenario-basierten Fragen. Die Prüfung deckt eine breite Palette von Themen ab, einschließlich Datenverarbeitungsarchitektur, Datenmodellierung, Datenübernahme, Datenverarbeitung, Datenanalyse und Datenvisualisierung. Die Prüfung testet auch die Fähigkeit des Kandidaten, mit verschiedenen Google Cloud Platform-Tools und -Services wie Google Cloud Storage, Google BigQuery und Google Cloud Dataflow zu arbeiten. Kandidaten, die die Prüfung bestehen, erhalten die Google Certified Professional Data Engineer-Zertifizierung, die ihre Expertise im Bereich Datenengineering und ihre Fähigkeit, Google Cloud Platform-Technologien zur Gestaltung und Implementierung von Datenverarbeitungssystemen zu nutzen, demonstriert.
>> Professional-Data-Engineer Praxisprüfung <<
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Die Google Professional-Data-Engineer-Prüfung besteht aus Multiple-Choice- und Multiple-Select-Fragen sowie Szenario-basierten Fragen, die vom Kandidaten Problemlösungsfähigkeiten erfordern. Die Prüfung dauert drei Stunden und die Kandidaten müssen mindestens 70% erreichen, um zu bestehen. Die Prüfungsgebühr beträgt 200 US-Dollar und sie ist auf Englisch, Japanisch und Spanisch verfügbar.
378. Frage
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
Antwort: A
Begründung:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage
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 toexpand 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.
379. Frage
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
Antwort: D
Begründung:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
380. Frage
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries.
What should you do?
Antwort: C
Begründung:
https://cloud.google.com/bigquery/docs/best-practices-costs
381. Frage
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
Antwort: B
382. Frage
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
Antwort: D
Begründung:
Dialogflow is used to do voice analytics on human computer interaction.
383. Frage
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Übrigens, Sie können die vollständige Version der PrüfungFrage Professional-Data-Engineer Prüfungsfragen aus dem Cloud-Speicher herunterladen: https://drive.google.com/open?id=1oj0C0Y8gs2yXPmarAVmx7i-oyJmLCV2C