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Google Professional-Data-Engineer Certification is highly valued in the industry, as it demonstrates the candidate's expertise in designing and building data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is recognized by organizations worldwide, and it can help professionals advance their careers and earn higher salaries. Additionally, the certification provides access to exclusive Google Cloud resources and updates, which can help professionals stay up-to-date with the latest advancements in data engineering.

Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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Google Certified Professional Data Engineer Exam Sample Questions (Q357-Q362):

NEW QUESTION # 357
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 two.)

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Use Cloud Deployment Manager to automate access provision.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
E. 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.

Answer: A,C

Explanation:
Google suggests that we should provide access by following google hierarchy and groups for users with similar roles.

NEW QUESTION # 358
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 number of workers
B. The maximum number of workers
C. The zone
D. The disk size per worker

Answer: C

NEW QUESTION # 359
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's dat a. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

A. Create authorized datasets to publish shared data in the subscribing team's project.
B. Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on the dataset.
C. Use Analytics Hub to facilitate data sharing.
D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

Answer: D

Explanation:
To provide a cost-effective storage and processing solution that allows data scientists to explore data similarly to using the on-premises HDFS cluster with SQL on the Hive query engine, deploying a Dataproc cluster is the best choice. Here's why:
Compatibility with Hive:
Dataproc is a fully managed Apache Spark and Hadoop service that provides native support for Hive, making it easy for data scientists to run SQL queries on the data as they would in an on-premises Hadoop environment.
This ensures that the transition to Google Cloud is smooth, with minimal changes required in the workflow.
Cost-Effective Storage:
Storing the ORC files in Cloud Storage is cost-effective and scalable, providing a reliable and durable storage solution that integrates seamlessly with Dataproc.
Cloud Storage allows you to store large datasets at a lower cost compared to other storage options.
Hive Integration:
Dataproc supports running Hive directly, which is essential for data scientists familiar with SQL on the Hive query engine.
This setup enables the use of existing Hive queries and scripts without significant modifications.
Steps to Implement:
Copy ORC Files to Cloud Storage:
Transfer the ORC files from the on-premises HDFS cluster to Cloud Storage, ensuring they are organized in a similar directory structure.
Deploy Dataproc Cluster:
Set up a Dataproc cluster configured to run Hive. Ensure that the cluster has access to the ORC files stored in Cloud Storage.
Configure Hive:
Configure Hive on Dataproc to read from the ORC files in Cloud Storage. This can be done by setting up external tables in Hive that point to the Cloud Storage location.
Provide Access to Data Scientists:
Grant the data scientist team access to the Dataproc cluster and the necessary permissions to interact with the Hive tables.
Reference:
Dataproc Documentation
Hive on Dataproc
Google Cloud Storage Documentation

NEW QUESTION # 360
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?

A. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
B. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
C. Create an additional project to overcome the 2K on-demand per-project quota.
D. Convert your batch BQ queries into interactive BQ queries.

Answer: B

NEW QUESTION # 361
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

A. Use Cloud Data Fusion to move files into Cloud Storage.
B. Use BigQuery Data Transfer Service to move files into BigQuery.
C. Use Storage Transfer Service to move files into Cloud Storage.
D. Use Dataflow to move files into Cloud Storage.

Answer: A

Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
* Cloud Data Fusion:
* Cloud Data Fusionis a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
* It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
* Custom Encryption Keys:
* Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
* Centralizing Data:
* Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
* Set Up Cloud Data Fusion:
* Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
* Create ETL Pipelines:
* Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
* Run and Monitor Pipelines:
* Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference Links:
* Cloud Data Fusion Documentation
* Using Customer-Managed Encryption Keys (CMEK)

NEW QUESTION # 362
......

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2025 Useful Google Professional-Data-Engineer Flexible Testing Engine

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Hot Professional-Data-Engineer Flexible Testing Engine | Authoritative Valid Professional-Data-Engineer Exam Guide and Updated Valid Dumps Google Certified Professional Data Engineer Exam Files

Google Certified Professional Data Engineer Exam Sample Questions (Q357-Q362):

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