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NEW QUESTION: 1
Your customer wants to be notified of SnapMirror lag errors within 15 minutes. The customer is not interested in maintaining custom scripts.
How would you accomplish this task?
A. Use OnCommand System Manager to notify on Protection Health errors.
B. Use OnCommand Unified Manager to generate Volume Transfer Status reports on a schedule.
C. Use OnCommand Unified Manager to create an error alert for SnapMirror Relationship lag.
D. Use OnCommand System Manager to create a warning alert for SnapMirror lag.
Answer: C

NEW QUESTION: 2
While SnapView snapshots are being used, what happens when the reserved LUN pool fills to 100% utilization?
A. the snapshot ml become corrupted
B. The snapshot session is quiesced
C. The snapshot is deleted
D. The snapshot session stops
Answer: D
Explanation:
Explanation/Reference:
Explanation:
SnapView sessions are stopped automatically by the software if the reserved LUN pool fills.
The session that caused the LUN pool to fill is terminated. If multiple sessions are running on a single source LUN, all sessions that use the chunk that caused the overflow will be terminated.

NEW QUESTION: 3
A company sells large batches of chemicals. Item specifications vary from batch to batch.
You must fulfill multiple orders from the same batch of chemicals. If a single batch cannot be reserved, manual reservation must be used. The system must also make reservations based on the earliest batch receipt date.
You need to set up a new chemical item that automatically uses the new batch reservation logic.
Which three settings should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. In the Tracking dimension group, select Coverage plan by dimension for batch number.
B. In the Item model group, select the FIFO inventory model.
C. In the Storage dimension group, select Coverage plan by dimension for the site and warehouse dimensions.
D. In the Item model group, select Same batch reservation and Consolidate requirements.
E. In the Item model group, select FEFO date-controlled.
Answer: A,B,D
Explanation:
Reference:
https://docs.microsoft.com/en-us/dynamics365/supply-chain/sales-marketing/reserve-same-batch-sales-order

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.
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?
A. Store the common data encoded as Avro in Google Cloud Storage.
B. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
C. Store the common data in BigQuery and expose authorized views.
D. Store the common data in BigQuery as partitioned tables.
Answer: C