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| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
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NEW QUESTION # 95
You are defining the data governance strategy for a new BigQuery table with medical and financial data. You want a scalable solution that ensures the clinical researchers can access patient medical data without financial information, while allowing the accounting team to access only financial data with minimal patient identifiers. What should you do?
Answer: A
Explanation:
Authorized views allow you to expose only the approved columns or rows from a BigQuery table to specific groups, enabling scalable, fine-grained access control for different teams without duplicating the underlying data.
NEW QUESTION # 96
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.
Answer: C,D
NEW QUESTION # 97
You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
* Data engineers, which require lull data lake access
* Analytic users, which require access to curated data
You need to assign access rights to these two groups. What should you do?
Answer: B
Explanation:
When designing a data mesh on Google Cloud using Dataplex to manage data in BigQuery and Cloud Storage, it is essential to simplify data asset permissions while ensuring that each user group has the appropriate access levels. Here's why option A is the best choice:
Data Engineer Group:
Data engineers require full access to the data lake to manage and operate data assets comprehensively.
Granting the dataplex.dataOwner role to the data engineer group on the customer data lake ensures they have the necessary permissions to create, modify, and delete data assets within the lake.
Analytic User Group:
Analytic users need access to curated data but do not require full control over all data assets. Granting the dataplex.dataReader role to the analytic user group on the customer curated zone provides read-only access to the curated data, enabling them to analyze the data without the ability to modify or delete it.
Steps to Implement:
Grant Data Engineer Permissions:
Assign the dataplex.dataOwner role to the data engineer group on the customer data lake to ensure full access and management capabilities.
Grant Analytic User Permissions:
Assign the dataplex.dataReader role to the analytic user group on the customer curated zone to provide read- only access to curated data.
Reference Links:
Dataplex IAM Roles and Permissions
Managing Access in Dataplex
NEW QUESTION # 98
Case Study: 1 - Flowlogistic
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs 60 virtual machines across 20 physical servers Tomcat - Java services Nginx - static content Batch servers Storage appliances iSCSI for virtual machine (VM) hosts Fibre Channel storage area network (FC SAN) ?SQL server storage Network-attached storage (NAS) image storage, logs, backups Apache Hadoop /Spark servers Core Data Lake Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production. Aggregate data in a centralized Data Lake for analysis Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability.
Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
Answer: B
NEW QUESTION # 99
Which of the following is not true about Dataflow pipelines?
Answer: B
Explanation:
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms
NEW QUESTION # 100
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