START EXAM PREPARATION WITH REAL AND VALID GOOGLE PROFESSIONAL-DATA-ENGINEER EXAM QUESTIONS​

Start Exam Preparation with Real and Valid Google Professional-Data-Engineer Exam Questions​

Start Exam Preparation with Real and Valid Google Professional-Data-Engineer Exam Questions​

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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is an essential certification exam for professionals looking to advance their careers in the field of data engineering. Passing Professional-Data-Engineer exam validates a candidate's expertise in designing, building, and managing data processing systems. It also demonstrates their ability to analyze and interpret data, make informed business decisions, and leverage cloud-based data processing systems to achieve business objectives.

Google Professional-Data-Engineer Certification Exam covers a broad range of topics, including data processing systems, data modeling, data analysis, data visualization, and machine learning. It requires a strong understanding of Google Cloud Platform products and services, such as BigQuery, Dataflow, Dataproc, and Pub/Sub. Professional-Data-Engineer exam also tests the ability to design and implement solutions that are scalable, efficient, and secure.

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

  • Designing data processing systems
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • Schema design
  • Data publishing and visualization (e.g., BigQuery)
  • Selecting the appropriate storage technologies
  • Capacity planning
  • Designing data pipelines
  • At least once, in-order, and exactly once, etc., event processing
  • Hybrid cloud and edge computing
  • Distributed systems
  • Choice of infrastructure
  • Job automation and orchestration (e.g., Cloud Composer)
  • Use of distributed systems
  • Data modeling
  • System availability and fault tolerance
  • Tradeoffs involving latency, throughput, transactions
  • Online (interactive) vs. batch predictions

Google Certified Professional Data Engineer Exam Sample Questions (Q152-Q157):

NEW QUESTION # 152
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

  • A. Use a higher-memory node so that the job runs faster
  • B. Migrate the workload to Google Cloud Dataflow
  • C. Use SSDs on the worker nodes so that the job can run faster
  • D. Use pre-emptible virtual machines (VMs) for the cluster

Answer: B

Explanation:
Explanation


NEW QUESTION # 153
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

  • A. Create an authorized view that contains the CMEK to decrypt the data when accessed.
  • B. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.
  • C. Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.
  • D. Provide the partner organization a copy of your CMEKs to decrypt the data.

Answer: C

Explanation:
If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. Reference:
Customer-managed Cloud KMS keys
[Authorized views]
[Analytics Hub overview]
[Creating an Analytics Hub listing]


NEW QUESTION # 154
You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

  • A. Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.
  • B. Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset
  • C. Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.
  • D. Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Answer: B

Explanation:
The BigQuery Data Viewer role allows users to read data and metadata from tables and views, but not to modify or delete them. By giving analysts this role on the shared dataset, you can ensure that they can access the data for analysis, but not change it. The BigQuery Data Editor role allows users to create, update, and delete tables and views, as well as read and write data. By giving analysts this role at the dataset level for their assigned dataset, you can provide them with individual workspaces where they can store their own tables and views, without affecting the shared dataset or other analysts' datasets. This way, you can achieve both data protection and data isolation for your team. References:
* BigQuery IAM roles and permissions
* Basic roles and permissions


NEW QUESTION # 155
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

  • A. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
  • B. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
  • C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
  • D. Use the NOW () function in BigQuery to record the event's time.

Answer: A

Explanation:
Topic 3, 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.


NEW QUESTION # 156
Your neural network model is taking days to train. You want to increase the training speed. What can you
do?

  • A. Subsample your test dataset.
  • B. Increase the number of layers in your neural network.
  • C. Increase the number of input features to your model.
  • D. Subsample your training dataset.

Answer: B

Explanation:
Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d


NEW QUESTION # 157
......

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