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下載Professional-Data-Engineer考試題庫
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下載Google Certified Professional Data Engineer Exam考試題庫
NEW QUESTION 50
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?
- A. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
- B. Assign the users/groups data viewer access at the table level for each table
- C. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
- D. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
Answer: B
NEW QUESTION 51
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?
- A. Install the StackDriver Agent and configure the MySQL plugin.
- B. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
- C. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
- D. Place the MariaDB instances in an Instance Group with a Health Check.
Answer: C
NEW QUESTION 52
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.
- A. HTTPS
- B. SOCKS
- C. HTTP
- D. VPN
Answer: B
Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
NEW QUESTION 53
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data.
Which two actions should you take? (Choose two.)
- A. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
- B. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
- C. Configure your Cloud Dataflow pipeline to use local execution
- D. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
- E. Increase the number of nodes in the Cloud Bigtable cluster
Answer: B,D
Explanation:
A – Local execution is useful for testing and debugging purposes, especially if your pipeline can use smaller in-memory datasets.
B- https://cloud.google.com/dataflow/docs/guides/specifying-exec-params C- increases both read and write performance D- Flatten merges multiple PCollection objects into a single logical PCollection.
E- Consider using CoGroupByKey if you have multiple data sets that provide information about related things .
NEW QUESTION 54
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
* 10 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?
- A. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
- C. Cloud Dataflow, Cloud SQL, and Cloud Storage
- D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- E. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
Answer: A
NEW QUESTION 55
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