NEW PROFESSIONAL-DATA-ENGINEER EXAM OBJECTIVES - PROFESSIONAL-DATA-ENGINEER CERTIFICATION TEST ANSWERS

New Professional-Data-Engineer Exam Objectives - Professional-Data-Engineer Certification Test Answers

New Professional-Data-Engineer Exam Objectives - Professional-Data-Engineer Certification Test Answers

Blog Article

Tags: New Professional-Data-Engineer Exam Objectives, Professional-Data-Engineer Certification Test Answers, Professional-Data-Engineer Dumps PDF, Valid Professional-Data-Engineer Test Registration, Professional-Data-Engineer Latest Learning Material

2025 Latest 2Pass4sure Professional-Data-Engineer PDF Dumps and Professional-Data-Engineer Exam Engine Free Share: https://drive.google.com/open?id=1X6QGamgmcCDk2D4wXr52-YzGEtsnz2t3

For candidates who are going to attend the exam, some practice is necessary, for the practice can build up the confidence. Professional-Data-Engineer exam torrent of us can help you pass the exam successfully. Professional-Data-Engineer exam braindumps are edited by professional experts, and the quality can be guaranteed. In addition, Professional-Data-Engineer exam materials cover most knowledge points for the exam, and you can master the major knowledge points for the exam, therefore your confidence for the exam will be strengthened. We provide you with free demo for you to have a try before buying Professional-Data-Engineer Exam Braindumps, so that you can know what the complete version is like.

If you are willing to clear exam successfully, you need to not only read books and study materials but also purchase Google Professional-Data-Engineer reliable exam cram for well-directed review which will make you half the work with double results. You can find three versions for each exam: PDF version, Software version and APP version. You can choose one or more versions of Professional-Data-Engineer Reliable Exam Cram based on your studying methods and habits.

>> New Professional-Data-Engineer Exam Objectives <<

Professional-Data-Engineer practice braindumps & Professional-Data-Engineer test prep cram

You can use this Google simulation software without an internet connection after installation. Tracking and reporting features of our Google Certified Professional Data Engineer Exam Professional-Data-Engineer Practice Exam software makes it easier for you to identify and overcome mistakes. Customization feature of this format allows you to change time limits and questions numbers of mock exams.

Google Certified Professional Data Engineer Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
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. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
  • 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. 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.

Answer: A


NEW QUESTION # 77
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.)

  • A. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
  • B. Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
  • C. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
  • D. Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
  • E. Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

Answer: B,D

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 # 78
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges.
Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

  • A. Enable query caching so you can cache data from previous months
  • B. Create separate views to cover each month, and query from these views
  • C. Convert the sharded tables into a single partitioned table
  • D. Convert all daily log tables into date-partitioned tables

Answer: D


NEW QUESTION # 79
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?