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| Section | Weight | Objectives |
|---|---|---|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
| Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Deploying and operationalizing the pipelines
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
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NEW QUESTION # 156
You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?
Answer: A
NEW QUESTION # 157
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?
Answer: A
Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
NEW QUESTION # 158
You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?
Answer: C
NEW QUESTION # 159
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: B
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 # 160
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
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?
Answer: D
NEW QUESTION # 161
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