New Google Professional-Data-Engineer Practice Materials & Professional-Data-Engineer Free Pdf Guide

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

To prepare for the Google Professional-Data-Engineer certification exam, candidates must have a solid understanding of data engineering concepts and techniques. They must also have experience working with the Google Cloud Platform and be familiar with the tools and services offered by Google. There are many resources available to help candidates prepare for the exam, including online courses, study guides, practice exams, and hands-on training.

Google Professional-Data-Engineer Exam is intended for professionals who work with data engineering, data integration, or data analysis. Professional-Data-Engineer exam tests the candidate's knowledge and understanding of Google Cloud Platform tools and services, including BigQuery, Cloud Dataflow, Cloud Pub/Sub, Cloud Storage, and more. Professional-Data-Engineer exam consists of multiple-choice questions and practical scenarios that test the candidate's ability to apply their knowledge and skills to real-world problems. Passing the exam and obtaining the certification demonstrates the individual's proficiency in designing and implementing scalable and reliable data processing systems using Google Cloud Platform technologies.

>> New Google Professional-Data-Engineer Practice Materials <<

New New Professional-Data-Engineer Practice Materials | Reliable Professional-Data-Engineer Free Pdf Guide: Google Certified Professional Data Engineer Exam

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Google Certified Professional Data Engineer Exam Sample Questions (Q264-Q269):

NEW QUESTION # 264
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. The original order identification number from the sales system, which is a monotonically increasing integer
B. The current epoch time
C. A concatenation of the product name and the current epoch time
D. A random universally unique identifier number (version 4 UUID)

Answer: D

NEW QUESTION # 265
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
B. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
C. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
D. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name

Answer: B

Explanation:
https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline#changing_windowing

NEW QUESTION # 266
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?

A. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
B. Increase the cluster size with more non-preemptible workers.
C. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
D. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Answer: C

Explanation:
Explanation
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION # 267
Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

A. multi-keyed data with very low latency
B. single-keyed data with very high latency
C. single-keyed data with very low latency
D. multi-keyed data with very high latency

Answer: C

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.

NEW QUESTION # 268
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 Dataflow, Cloud SQL, and Cloud Storage
B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
E. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer: B

Explanation:
Explanation

NEW QUESTION # 269
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New Google Professional-Data-Engineer Practice Materials & Professional-Data-Engineer Free Pdf Guide

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New New Professional-Data-Engineer Practice Materials | Reliable Professional-Data-Engineer Free Pdf Guide: Google Certified Professional Data Engineer Exam

Google Certified Professional Data Engineer Exam Sample Questions (Q264-Q269):