PROFESSIONAL-DATA-ENGINEER EXAM QUESTIONS FEE | PROFESSIONAL-DATA-ENGINEER LATEST TEST COST

Professional-Data-Engineer Exam Questions Fee | Professional-Data-Engineer Latest Test Cost

Professional-Data-Engineer Exam Questions Fee | Professional-Data-Engineer Latest Test Cost

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Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

Google Professional-Data-Engineer exam is designed to test an individual's knowledge and expertise in the field of data engineering. It is a certification offered by Google that recognizes professionals who have demonstrated their ability to design, build, and maintain data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam covers a wide range of topics, including data ingestion and processing, storage and data analysis, machine learning and data visualization.

The Google Professional-Data-Engineer exam consists of multiple-choice and multiple-select questions, and it lasts for two hours. Candidates have to answer a total of 50 questions, and the passing score is 70%. Professional-Data-Engineer exam is available in English and Japanese and can be taken online or at a testing center. The fee for the exam is $200, and it is valid for two years.

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Google Certified Professional Data Engineer Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

  • A. Cloud SQL
  • B. Cloud BigTable
  • C. Cloud Datastore
  • D. BigQuery

Answer: B

Explanation:
Explanation/Reference: https://cloud.google.com/solutions/business-intelligence/


NEW QUESTION # 66
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

  • A. the selection is final and you must resume using the same storage type
  • B. create a third instance and sync the data from the two storage types via batch jobs
  • C. run parallel instances where one is HDD and the other is SDD
  • D. export the data from the existing instance and import the data into a new instance

Answer: D

Explanation:
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another.
Reference:


NEW QUESTION # 67
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)

  • A. Ensure all the tables are included in global dataset.
  • B. Ensure each table is included in a dataset for a region.
  • C. Adjust the settings for each table to allow a related region-based security group view access.
  • D. Adjust the settings for each view to allow a related region-based security group view access.
  • E. Adjust the settings for each dataset to allow a related region-based security group view access.

Answer: B,E


NEW QUESTION # 68
You are building a model to make clothing recommendations. You know a user's fashion pis likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

  • A. Train on the existing data while using the new data as your test set.
  • B. Continuously retrain the model on a combination of existing data and the new data.
  • C. Train on the new data while using the existing data as your test set.
  • D. Continuously retrain the model on just the new data.

Answer: B

Explanation:
We have to use a combination of old and new test data as well as training data.


NEW QUESTION # 69
You want to encrypt the customer data stored in BigQuery. You need to implement for-user copyright-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions.
What should you do?

  • A. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
  • B. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
  • C. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
  • D. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

Answer: A

Explanation:
To implement for-user copyright-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
* Customer-Managed Encryption Keys (CMEK):
* CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
* Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
* For-User copyright-Deletion:
* For-user copyright-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
* Native Integration:
* Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
* Create a CMEK in Cloud KMS:
* Set up a new customer-managed encryption key in Cloud KMS.
* Associate the CMEK with BigQuery Tables:
* When creating a new table in BigQuery, specify the CMEK to be used for encryption.
* This can be done through the BigQuery console, CLI, or API.
Reference Links:
* BigQuery and CMEK
* Cloud KMS Documentation
* Encrypting Data in BigQuery


NEW QUESTION # 70
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