Marvelous Professional-Data-Engineer Pass4sure Dumps Pdf – Find Shortcut to Pass Professional-Data-Engineer Exam

Marvelous Professional-Data-Engineer Pass4sure Dumps Pdf – Find Shortcut to Pass Professional-Data-Engineer Exam

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The Google Professional-Data-Engineer certification exam is designed for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. As a Google Certified Professional Data Engineer, you will be recognized as an expert in the field and have the skills to create scalable and efficient data processing systems that leverage Google Cloud technologies. This certification is ideal for data engineers, data architects, solutions architects, and anyone who wants to validate their skills in building and managing data processing systems in the cloud.

Ensure Solution Quality

  • Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
  • Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
  • Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
  • Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;

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Google Certified Professional Data Engineer Exam Sample Questions (Q241-Q246):

You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

  • A. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
  • B. Increase the share of the test sample in the train-test split.
  • C. Try to collect more data and increase the size of your dataset.
  • D. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Answer: A

You are designing the database schema for a machine learning-based food ordering service that will
predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn’t like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data
schema. Which Google Cloud Platform product should you use?

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

Answer: D

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
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

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
  • B. 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.
  • 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 Sheets, use formulas to calculate a metric, and use filters/sorting to show
    only suboptimal links in a table.

Answer: B

Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

  • A. Cloud Bigtable
  • B. Google Cloud Storage
  • C. Google BigQuery
  • D. Google Cloud Datastore

Answer: A

https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using- gcp

Which of these operations can you perform from the BigQuery Web UI?

  • A. Upload multiple files using a wildcard.
  • B. Upload a file in SQL format.
  • C. Load data with nested and repeated fields.
  • D. Upload a 20 MB file.

Answer: C

You can load data with nested and repeated fields using the Web UI.
You cannot use the Web UI to:
– Upload a file greater than 10 MB in size
– Upload multiple files at the same time
– Upload a file in SQL format
All three of the above operations can be performed using the “bq” command.
Reference: https://cloud.google.com/bigquery/loading-data


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