Free PDF 2025 Google Professional-Data-Engineer Newest Related Certifications

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The Google Professional-Data-Engineer exam consists of multiple-choice questions and is divided into four sections. The first section covers designing data processing systems, including the ability to design and implement data storage systems, data processing workflows, and data pipelines. The second section covers designing machine learning models, including the ability to design and implement machine learning algorithms, and to use machine learning to solve business problems. The third section covers designing data analysis tools, including the ability to design and implement data visualization tools, and to use data analysis to solve business problems. The fourth and final section covers designing applications and services, including the ability to design and implement cloud-based applications and services that meet the needs of businesses.

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 Professional-Data-Engineer certification exam is a rigorous and comprehensive exam that requires individuals to have a deep understanding of data engineering technologies and concepts. Professional-Data-Engineer exam consists of multiple choice and scenario-based questions that assess an individual's ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer Exam is timed and individuals have a limited amount of time to complete the exam. To pass the exam, individuals must score 70% or higher.

Google Certified Professional Data Engineer Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
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. Update the job to increase the maximum number of workers.
B. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
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: B

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. Reference:
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 # 15
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. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C. 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.
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,B

NEW QUESTION # 16
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

A. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.
B. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.
C. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.
D. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

Answer: B

Explanation:
The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files. Reference: https://cloud.google.com/memorystore/docs/redis/import-export-overview

NEW QUESTION # 17
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 and share an authorized view that provides the aggregate results.
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 dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer: D

Explanation:
Authorized view is used to protect the underlying data, authorized views are created in different dataset with restricted access.

NEW QUESTION # 18
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
B. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
C. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
D. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Answer: A

NEW QUESTION # 19
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Free PDF 2025 Google Professional-Data-Engineer Newest Related Certifications

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Google Certified Professional Data Engineer Exam Sample Questions (Q14-Q19):

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