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| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Operationalizing machine learning models | 26% | - Model deployment and monitoring
|
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NEW QUESTION # 381
Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?
Answer: D
Explanation:
To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
Analytics Hub:
Analytics Hub allows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
Data Publishing and Subscribing:
Teams can publish datasets they control, allowing them to manage access and updates independently.
Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
Minimized Operational Tasks and Costs:
This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
Steps to Implement:
Set Up Analytics Hub:
Enable Analytics Hub in your Google Cloud project.
Provide training to teams on how to publish and subscribe to data exchanges.
Publish Data:
Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
Subscribe to Data:
Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
Reference:
Analytics Hub Documentation
Publishing Data in Analytics Hub
Subscribing to Data in Analytics Hub
NEW QUESTION # 382
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select
2 answers)?
Answer: A,C
Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of
100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms
NEW QUESTION # 383
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
* The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
* Support for publish/subscribe semantics on hundreds of topics
* Retain per-key ordering
Which system should you choose?
Answer: B
Explanation:
These are the functionalities which are currently lagging/not-available with Pub/Sub.
NEW QUESTION # 384
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
Answer: D
Explanation:
Bigquery is used to analyse access logs, data access logs capture the details of the user that accessed the data.
NEW QUESTION # 385
You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet.
What should you do?
Answer: B
Explanation:
To securely ingest data from an on-premises MySQL database into BigQuery without routing through the public internet, using Datastream with Private connectivity over Cloud Interconnect is the best approach.
Here's why:
* Datastream for Data Replication:
* Datastream provides a managed service for data replication from various sources, including on- premises databases, to Google Cloud services like BigQuery.
* Cloud Interconnect:
* Cloud Interconnect establishes a private connection between your on-premises data center and Google Cloud, ensuring that data transfer occurs over a secure, private network rather than the public internet.
* Private Connectivity:
* Using Private connectivity with Datastream leverages the established Cloud Interconnect to securely connect your on-premises MySQL database with Google Cloud. This method ensures that the data does not traverse the public internet.
* Encryption:
* Using Server-only encryption ensures that data is encrypted in transit between Datastream and BigQuery, adding an extra layer of security.
Steps to Implement:
* Set Up Cloud Interconnect:
* Establish a Cloud Interconnect between your on-premises data center and Google Cloud to create a private connection.
* Configure Datastream:
* Set up Datastream to use Private connectivity as the connection method and allocate an IP address range within your VPC network.
* Use Server-only encryption to ensure secure data transfer.
* Create Connection Profile:
* Create a connection profile in Datastream to define the connection parameters, including the use of Cloud Interconnect and Private connectivity.
Reference Links:
* Datastream Documentation
* Cloud Interconnect Documentation
* Setting Up Private Connectivity in Datastream
NEW QUESTION # 386
......
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