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| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems (~30% of the exam) | 30% | - Designing data pipelines
|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Building and maintaining data structures and databases
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
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NEW QUESTION # 429
You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?
Answer: B
Explanation:
Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project. Reference:
https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc
NEW QUESTION # 430
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
Answer: C
Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.
Reference:
https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options
NEW QUESTION # 431
Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?
Answer: D
Explanation:
Topic 2, Flowlogistic Case Study
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.
NEW QUESTION # 432
You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum.
What should you do?
Answer: C
Explanation:
* Dataproc's Compatibility with Apache Spark:Dataproc is a managed service for running Hadoop and Spark clusters on Google Cloud. This means it is designed to seamlessly run Apache Spark jobs with minimal code changes. Your existing Spark jobs should run on Dataproc with little to no modification.
* Cloud Storage as a Scalable Data Lake:Cloud Storage provides a highly scalable and durable storage solution for your data. It's designed to handle large volumes of data that Spark jobs typically process.
* Minimizing Operational Overhead:By using Dataproc, you eliminate the need to manage and maintain a Hadoop cluster yourself. Google Cloud handles the infrastructure, allowing you to focus on your data processing tasks.
* Tight Timeline and Minimal Code Changes:This option directly addresses the requirements of the question. It offers a quick and easy way to migrate your Spark jobs to Google Cloud with minimal disruption to your existing codebase.
Why other options are not suitable:
* A. Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances:This option requires you to manage the underlying infrastructure yourself, which contradicts the requirement of using managed services.
* C. Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach:
While BigQuery is a powerful data warehouse, converting Spark scripts to SQL would require substantial code changes and might not be feasible within a tight timeline.
* D. Rewrite your jobs in Apache Beam. Run your jobs in Dataflow:Rewriting jobs in Apache Beam would be a significant undertaking and not suitable for a quick migration with minimal code changes.
NEW QUESTION # 433
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? Choose 2 answers.
Answer: A,E
NEW QUESTION # 434
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