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| Section | Weight | Objectives |
|---|---|---|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Designing data processing systems | 22% | - Data architecture and storage design
|
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NEW QUESTION # 96
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
Answer: A
Explanation:
References:
NEW QUESTION # 97
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: C
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 # 98
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.
Answer: C
Explanation:
Explanation
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/
NEW QUESTION # 99
You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
Answer: C
Explanation:
To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
Cloud Functions:
Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
Flexibility and Simplicity:
Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
Performance:
Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
Steps to Implement:
Write the Cloud Function:
Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
Deploy the function to Google Cloud.
Invoke Cloud Function from Workflow:
Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
steps:
- callCloudFunction:
call: http.post
args:
url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
body:
key: value
Process Results:
Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
Reference:
Google Cloud Functions Documentation
Using Workflows with Cloud Functions
Workflows Standard Library
NEW QUESTION # 100
You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?
Answer: D
NEW QUESTION # 101
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