VALID PROFESSIONAL-DATA-ENGINEER EXAM PAPERS - KEY PROFESSIONAL-DATA-ENGINEER CONCEPTS

Valid Professional-Data-Engineer Exam Papers - Key Professional-Data-Engineer Concepts

Valid Professional-Data-Engineer Exam Papers - Key Professional-Data-Engineer Concepts

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Tags: Valid Professional-Data-Engineer Exam Papers, Key Professional-Data-Engineer Concepts, Test Professional-Data-Engineer Lab Questions, Reliable Professional-Data-Engineer Test Questions, Reliable Professional-Data-Engineer Test Labs

If you want to choose passing Google certification Professional-Data-Engineer exam to make yourself have a more stable position in today's competitive IT area and the professional ability become more powerful, you must have a strong expertise. And passing Google certification Professional-Data-Engineer exam is not very simple. Perhaps passing Google Certification Professional-Data-Engineer Exam is a stepping stone to promote yourself in the IT area, but it doesn't need to spend a lot of time and effort to review the relevant knowledge, you can choose to use our BraindumpsIT product, a training tool prepared for the IT certification exams.

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

>> Valid Professional-Data-Engineer Exam Papers <<

Key Professional-Data-Engineer Concepts - Test Professional-Data-Engineer Lab Questions

In the process of using Professional-Data-Engineer study question if the clients encounter the difficulties, the obstacles and the doubts they could contact our online customer service staff in the whole day. Our service team will update the Professional-Data-Engineer certification file periodically and provide one-year free update. Have known these advantages you may be curious to further understand the detailed information about our Professional-Data-Engineer training braindump and we list the detailed characteristics and functions of our Professional-Data-Engineer exam questions on the web for you to know.

Google Certified Professional Data Engineer Exam Sample Questions (Q255-Q260):

NEW QUESTION # 255
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled.
You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

  • A. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
  • B. Update the job to increase the maximum number of workers.
  • C. Enable Vertical Autoscaling to let the pipeline use larger workers.
  • D. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Answer: D

Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage.
This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. References:
* 1: Streaming pipelines
* 2: Batch vs Streaming Performance in Google Cloud Dataflow
* 3: Deploy Dataflow pipelines
* 4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
* 5: Managing costs for Dataflow batch and streaming data processing


NEW QUESTION # 256
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding What should you do?

  • A. Create a Spark job and submit it to Dataproc Serverless.
  • B. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
  • C. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
  • D. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.

Answer: C

Explanation:
Cloud Data Fusion is a fully managed, cloud-native data integration service that allows you to build and manage data pipelines with a graphical interface. Wrangler is a feature of Cloud Data Fusion that enables you to interactively explore, clean, and transform data using a spreadsheet-like UI. You can use Wrangler to normalize the data in BigQuery by applying various directives, such as parsing, formatting, replacing, and validating data. You can also preview the results and export the wrangled data to BigQuery or other destinations. You can then set up a recurring job in Cloud Data Fusion to run the Wrangler pipeline on a schedule, such as weekly or daily. This way, you can create a quick and code-free solution to normalize the data for your reports. References:
* Cloud Data Fusion overview
* Wrangler overview
* Wrangle data from BigQuery
* [Scheduling pipelines]


NEW QUESTION # 257
You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

  • A. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.
  • B. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.
  • C. Change the data pipeline to use BigQuery for storing stock trades, and update your application.
  • D. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Answer: B


NEW QUESTION # 258
An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

  • A. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
  • B. Create and share a new dataset and table that contains the aggregate results.
  • C. Create and share a new dataset and view that provides the aggregate results.
  • D. Create and share an authorized view that provides the aggregate results.

Answer: A

Explanation:
Reference: https://cloud.google.com/bigquery/docs/access-control


NEW QUESTION # 259
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

  • A. Submit duplicate pipelines in two different zones by using the -zone flag.
  • B. Specify a worker region by using the -region flag.
  • C. Set the pipeline staging location as a regional Cloud Storage bucket.
  • D. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

Answer: C

Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the -zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. Reference:
1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
3: Regional endpoints | Cloud Dataflow | Google Cloud


NEW QUESTION # 260
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Key Professional-Data-Engineer Concepts: https://www.braindumpsit.com/Professional-Data-Engineer_real-exam.html

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