PROFESSIONAL-DATA-ENGINEER BOOK FREE - PROFESSIONAL-DATA-ENGINEER VALID DUMPS

Professional-Data-Engineer Book Free - Professional-Data-Engineer Valid Dumps

Professional-Data-Engineer Book Free - Professional-Data-Engineer Valid Dumps

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Google Professional-Data-Engineer certification involves passing a rigorous exam that tests the candidate's knowledge and skills in several areas, including data processing systems, data analysis, data modeling, machine learning, and data visualizations. Professional-Data-Engineer exam is designed to assess the candidate's ability to design, build, and maintain data processing systems that can handle large quantities of data and provide accurate insights.

Google Professional-Data-Engineer exam is designed to test an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer Exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

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Google Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
To give a user read permission for only the first three columns of a table, which access control method would you use?

  • A. It's not possible to give access to only the first three columns of a table.
  • B. Predefined role
  • C. Primitive role
  • D. Authorized view

Answer: D

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views


NEW QUESTION # 82
What are two of the characteristics of using online prediction rather than batch prediction?

  • A. Predictions are returned in the response message.
  • B. It is optimized to handle a high volume of data instances in a job and to run more complex models.
  • C. Predictions are written to output files in a Cloud Storage location that you specify.
  • D. It is optimized to minimize the latency of serving predictions.

Answer: A,D

Explanation:
Online prediction
.Optimized to minimize the latency of serving predictions.
.Predictions returned in the response message.
Batch prediction
.Optimized to handle a high volume of instances in a job and to run more complex models.
.Predictions written to output files in a Cloud Storage location that you specify.


NEW QUESTION # 83
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

  • A. Retry the query with exponential backoff, up to a cap of 15 minutes.
  • B. Reduce the query frequency to once every hour until the database comes back online.
  • C. Retry the query every second until it comes back online to minimize staleness of data.
  • D. Issue a command to restart the database servers.

Answer: A

Explanation:
https://cloud.google.com/sql/docs/mysql/manage-connections


NEW QUESTION # 84
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


NEW QUESTION # 85
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 need to compose visualizations for operations teams with the following requirements:
* The report must include telemetry data from all 50,000 installations for the most resent 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.
Which approach meets the requirements?

  • A. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
  • B. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
  • C. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
  • D. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Answer: A


NEW QUESTION # 86
......

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