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Google Professional-Data-Engineer Certification is a valuable credential that can help professionals stand out in the competitive field of data engineering. It demonstrates that the certified individual has the expertise and skills required to design, build, and manage data processing systems effectively. As data becomes increasingly important in organizations of all sizes and industries, the demand for certified data engineers is expected to grow, making this certification a worthwhile investment for individuals looking to advance their career in the field.
NEW QUESTION # 274
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
Answer: B
NEW QUESTION # 275
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
* Executing the transformations on a schedule
* Enabling non-developer analysts to modify transformations
* Providing a graphical tool for designing transformations
What should you do?
Answer: A
NEW QUESTION # 276
Cloud Dataproc charges you only for what you really use with _____ billing.
Answer: C
Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.
NEW QUESTION # 277
You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?
Answer: A
Explanation:
To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:
* Cloud Data Loss Prevention (DLP):
* Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre- defined infoTypes for various kinds of sensitive data, including street addresses.
* Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.
* Deep Inspection Job:
* A deep inspection job allows you to scan entire tables for sensitive information.
* By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.
* Scalability and Accuracy:
* Cloud DLP is scalable and can handle large datasets efficiently.
* It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.
Steps to Implement:
* Set Up Cloud DLP:
* Enable the Cloud DLP API in your Google Cloud project.
* Create an Inspection Template:
* Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.
* Run Deep Inspection Jobs:
* Create and run a deep inspection job for each table in your dataset using the inspection template.
* Review the inspection job results to retrieve all occurrences of street addresses.
Reference Links:
* Cloud DLP Documentation
* Creating Inspection Jobs
NEW QUESTION # 278
Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?
Answer: B
Explanation:
Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.
Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub
/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub
/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM |Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAMDocumentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].
NEW QUESTION # 279
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