Quiz Google - Professional-Data-Engineer Pass-Sure Test Vce

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The Google Certified Professional Data Engineer Exam certification exam is divided into multiple sections, each of which covers a specific area of data engineering. Professional-Data-Engineer exam is scored on a scale of 1000, with a passing score of 700 or higher. Professional-Data-Engineer Exam is computer-based and can be taken at a testing center or online. The cost of the exam is $200, and it is valid for two years.

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Google Certified Professional Data Engineer Exam Sample Questions (Q290-Q295):

NEW QUESTION # 290
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Workflow Templates on Cloud Dataproc
C. Cloud Composer
D. cron

Answer: C

NEW QUESTION # 291
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 is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

A. Cloud Datastore and Cloud Bigtable
B. BigQuery and Cloud Bigtable
C. Cloud Bigtable and Cloud SQL
D. BigQuery and Cloud Storage

Answer: B

NEW QUESTION # 292
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. Copy the tables you need to share to a dataset without CMEKs Create an Analytics Hub listing for this dataset.
B. Provide the partner organization a copy of your CMEKs to decrypt the data.
C. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights. viewer role on the bucket to the partner organization.
D. Create an authorized view that contains the CMEK to decrypt the data when accessed.

Answer: A

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. References:
* Customer-managed Cloud KMS keys
* [Authorized views]
* [Analytics Hub overview]
* [Creating an Analytics Hub listing]

NEW QUESTION # 293
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

Answer: A

NEW QUESTION # 294
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

A. Train on the existing data while using the new data as your test set.
B. Continuously retrain the model on just the new data.
C. Train on the new data while using the existing data as your test set.
D. Continuously retrain the model on a combination of existing data and the new data.

Answer: A

Explanation:
https://cloud.google.com/automl-tables/docs/prepare

NEW QUESTION # 295
......

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