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| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Certified Professional Data Engineer Exam |
| Exam Number: | Professional Data Engineer |
| Related Certifications: | Google Cloud Certified - Associate Cloud Engineer Google Cloud Certified - Professional Cloud Architect Google Cloud Certified - Professional Data Analytics Engineer Google Cloud Certified - Professional Machine Learning Engineer |
| Passing Score: | Not publicly disclosed (scaled scoring system) |
| Available Languages: | German, Japanese, English, Portuguese, French, Spanish |
| Exam Price: | $200 USD |
| Exam Format: | multiple choice, case study, multiple select |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | Approximately 50–60 multiple choice and multiple select questions |
| Certificate Validity Period: | 2 years |
| Recommended Training: | Google Cloud Data Engineering Learning Path Google Cloud Professional Data Engineer Course (Coursera) |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam or onsite test center (Kryterion Webassessor) |
| Pre Condition: | No formal prerequisites required; recommended experience with data processing and Google Cloud Platform services |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.
Google Professional-Data-Engineer certification exam is designed to validate the skills and knowledge of individuals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is intended for those professionals who have expertise in designing, building, and maintaining data processing systems using Google Cloud Platform services. Professional-Data-Engineer exam evaluates the candidates' ability to design, implement, and manage data processing systems, as well as their understanding of data analysis and machine learning concepts.
The Google Professional-Data-Engineer Exam consists of multiple-choice questions and is divided into four sections. The first section covers designing data processing systems, including the ability to design and implement data storage systems, data processing workflows, and data pipelines. The second section covers designing machine learning models, including the ability to design and implement machine learning algorithms, and to use machine learning to solve business problems. The third section covers designing data analysis tools, including the ability to design and implement data visualization tools, and to use data analysis to solve business problems. The fourth and final section covers designing applications and services, including the ability to design and implement cloud-based applications and services that meet the needs of businesses.
NEW QUESTION # 87
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
Answer: D
Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
NEW QUESTION # 88
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?
Answer: B
Explanation:
To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:
* Compatibility with Hive:
* Dataprocis a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on- premises Hadoop environment.
* This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.
* Cost-Effective Storage:
* Storing the ORC files inCloud Storageis cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.
* Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.
* Hive Integration:
* Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.
* This setup enables the use of existing Hive queries and scripts without significant modifications.
Steps to Implement:
* Copy ORC Files to Cloud Storage:
* Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.
* Deploy Dataproc Cluster:
* Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.
* Configure Hive:
* Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.
* Provide Access to Data Scientists:
* Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.
Reference Links:
* Dataproc Documentation
* Hive on Dataproc
* Google Cloud Storage Documentation
NEW QUESTION # 89
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?
Answer: B
Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. References:
* Signed URLs | Cloud Storage Documentation
* V4 signing process with Cloud Storage tools
* V4 signing process withyour own program
* Using a URL list file
* What Is a 403 Forbidden Error (and How Can I Fix It)?
NEW QUESTION # 90
You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)
Answer: B,E
Explanation:
C -> Adding more nodes to a cluster (not replication) can improve the write performance
https://cloud.google.com/bigtable/docs/performance
D -> since Google recommends adding nodes when storage utilization is > 70%
https://cloud.google.com/bigtable/docs/modifying-instance#nodes
NEW QUESTION # 91
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.
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?
Answer: D
NEW QUESTION # 92
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