PROFESSIONAL-DATA-ENGINEER NEW DUMPS | PROFESSIONAL-DATA-ENGINEER RELIABLE TEST MATERIALS

Professional-Data-Engineer New Dumps | Professional-Data-Engineer Reliable Test Materials

Professional-Data-Engineer New Dumps | Professional-Data-Engineer Reliable Test Materials

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Google Professional-Data-Engineer exam is a comprehensive assessment that requires extensive preparation and study. It consists of 50 multiple-choice questions that need to be answered within two hours. Professional-Data-Engineer Exam Fee is $200, and it can be taken online or at a testing center. Professional-Data-Engineer exam is available in English, Japanese, Spanish, and Portuguese languages.

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The crucial thing when it comes to appearing a competitive exam like Professional-Data-Engineer knowing your problem-solving skills. And to do that you are going to need help from a Professional-Data-Engineer practice questions or braindumps. This is exactly what is delivered by our Professional-Data-Engineer test materials. The Professional-Data-Engineer Exam Dumps cover every topic of the actual Google certification exam. The Professional-Data-Engineer exam questions are divided into various groups and the candidate can solve these questions to test his skills and knowledge.

The Google Professional-Data-Engineer Exam Tests the candidate's proficiency in working with various data processing tools, including Google Cloud Platform technologies such as BigQuery, Dataflow, and Cloud Storage. Professional-Data-Engineer exam also covers topics such as data modeling, data ingestion, data transformation, and data analysis. Additionally, the exam tests the candidate's understanding of best practices for data engineering, including performance optimization, security, and compliance.

Google Certified Professional Data Engineer Exam Sample Questions (Q208-Q213):

NEW QUESTION # 208
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

  • A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
  • B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
  • C. Use Cloud Deployment Manager to automate access provision.
  • D. Introduce resource hierarchy to leverage access control policy inheritance.
  • E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: C,E


NEW QUESTION # 209
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
10 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's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

  • A. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
  • B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
  • C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
  • D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
  • E. Cloud Dataflow, Cloud SQL, and Cloud Storage

Answer: B


NEW QUESTION # 210
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

  • A. BigQuery cannot be accessed from local machines
  • B. Pipelines cannot be run locally
  • C. Your gcloud does not have access to the BigQuery resources
  • D. You are missing gcloud on your machine

Answer: C

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun


NEW QUESTION # 211
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-
series data in the cloud. This data is updated frequently and new data will be streaming in all the time.
Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this
data. Which product should they use to store the data?

  • A. Google Cloud Storage
  • B. Google Cloud Datastore
  • C. Cloud Bigtable
  • D. Google BigQuery

Answer: C

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series


NEW QUESTION # 212
You create an important report for your large team in Google Data Studio 360. The report uses Google
BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour
old. What should you do?

  • A. Refresh your browser tab showing the visualizations.
  • B. Disable caching in BigQuery by editing table details.
  • C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
  • D. Disable caching by editing the report settings.

Answer: D

Explanation:
Explanation/Reference:
Reference: https://support.google.com/datastudio/answer/7020039?hl=en


NEW QUESTION # 213
......

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