PROFESSIONAL-DATA-ENGINEER EXAMS & PROFESSIONAL-DATA-ENGINEER TESTKING LEARNING MATERIALS

Professional-Data-Engineer Exams & Professional-Data-Engineer Testking Learning Materials

Professional-Data-Engineer Exams & Professional-Data-Engineer Testking Learning Materials

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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.

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Google Certified Professional Data Engineer Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

  • A. The maximum number of workers
  • B. The number of workers
  • C. The zone
  • D. The disk size per worker

Answer: C


NEW QUESTION # 98
You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

  • A. Use hopping windows with a 15-mmute window, and a thirty-minute period.
  • B. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.
  • C. Use session windows with a 30-mmute gap duration.
  • D. Use session windows with a 15-minute gap duration.

Answer: D

Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary.
Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. References:
* Windowing concepts
* Session windows
* Windowing in Dataflow


NEW QUESTION # 99
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

  • A. Use Cloud Deployment Manager to automate access provision.
  • B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
  • C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
  • D. Introduce resource hierarchy to leverage access control policy inheritance.
  • E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: A,C


NEW QUESTION # 100
Which of the following is not possible using primitive roles?

  • A. Give UserA owner access and UserB editor access for all datasets in a project.
  • B. Give GroupA owner access and GroupB editor access for all datasets in a project.
  • C. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
  • D. Give a user access to view all datasets in a project, but not run queries on them.

Answer: D

Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles


NEW QUESTION # 101
The Dataflow SDKs have been recently transitioned into which Apache service?

  • A. Apache Hadoop
  • B. Apache Kafka
  • C. Apache Beam
  • D. Apache Spark

Answer: C

Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/


NEW QUESTION # 102
......

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