PROFESSIONAL-DATA-ENGINEER INSTANT DISCOUNT | PROFESSIONAL-DATA-ENGINEER TEST SCORE REPORT

Professional-Data-Engineer Instant Discount | Professional-Data-Engineer Test Score Report

Professional-Data-Engineer Instant Discount | Professional-Data-Engineer Test Score Report

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Google Professional-Data-Engineer Certification Exam is designed to validate the skills and knowledge of professionals who work with data on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification demonstrates a candidate’s expertise in designing, building, and maintaining data processing systems, as well as their ability to leverage the power of Google Cloud Platform to solve complex business problems.

Google Certified Professional Data Engineer Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
Cloud Bigtable is Google's ______ Big Data database service.

  • A. SQL Server
  • B. Relational
  • C. mySQL
  • D. NoSQL

Answer: D

Explanation:
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail. It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference: https://cloud.google.com/bigtable/


NEW QUESTION # 285
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real- time analysis of aggregated trends. What should you do?

  • A. Use bq loadto load a batch of sensor data every 60 seconds.
  • B. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
  • C. Use the INSERT statement to insert a batch of data every 60 seconds.
  • D. Use the MERGE statement to apply updates in batch every 60 seconds.

Answer: C

Explanation:
Explanation


NEW QUESTION # 286
MJTelco Case Study
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 need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

  • A. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
  • B. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
  • C. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
  • D. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Answer: D


NEW QUESTION # 287
Cloud Dataproc charges you only for what you really use with _____ billing.

  • A. minute-by-minute
  • B. month-by-month
  • C. hour-by-hour
  • D. week-by-week

Answer: A

Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
Reference: https://cloud.google.com/dataproc/docs/concepts/overview


NEW QUESTION # 288
You work for a financial institution that lets customers register online. As new customers register, their user data is sent to Pub/Sub before being ingested into BigQuery. For security reasons, you decide to redact your customers' Government issued Identification Number while allowing customer service representatives to view the original values when necessary. What should you do?

  • A. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic hash.
  • B. Use BigQuery column-level security. Set the table permissions so that only members of the Customer Service user group can see the SSN column.
  • C. Use BigQuery's built-in AEAD encryption to encrypt the SSN column. Save the keys to a new table that is only viewable by permissioned users.
  • D. Before loading the data into BigQuery, use Cloud Data Loss Prevention (DLP) to replace input values with a cryptographic format-preserving encryption token.

Answer: D


NEW QUESTION # 289
......

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