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NEW QUESTION: 1
You need to run a large data tree model by using rxDForest. The model must use cross validation.
Which rxDForest option should you use?
A. maxDepth
B. xVal
C. maxNumBins
D. maxCompete
E. maxSurrogate
Answer: B
Explanation:
References: https://docs.microsoft.com/en-us/machine-learning-server/r/how-to-revoscaler-decision-tree
NEW QUESTION: 2
Which two requirements must be satisfied to implement STP Bridge Assurance on Cisco
Nexus switches? (Choose two.)
A. STP or RSTP
B. a port type of network
C. MST or Rapid PVST+
D. a port type of normal
E. CST or RSTP
F. a port type of edge
Answer: B,C
NEW QUESTION: 3
トムは自分の組織のプロジェクトマネージャーです。彼のプロジェクトでは、最近、リスク対応計画を完成させました。彼は、コストとスケジュールのベースラインを更新する必要があることをマネージャーに伝えます。リスク対応計画により、トムはコストとスケジュールのベースラインを更新する必要があるのはなぜですか？
A. リスク対応の一環としての新規または省略された作業は、コストおよび/またはスケジュールのベースラインに変更を引き起こす可能性があります。
B. リスクレスポンスの実装には、時間と費用がかかる場合があります。
C. ベースラインを更新するのではなく、バージョンを介して調整する必要があります。
D. リスク対応は、プロジェクトの時間と投資を保護します。
Answer: A
NEW QUESTION: 4
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.
is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster.
A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
A. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
B. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
D. Use the NOW () function in BigQuery to record the event's time.
Answer: A