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NEW QUESTION: 1
An administrator has been tasked with enabling High Availability (HA) on a cluster in a vSphere 6.x
environment with default settings. The cluster configures properly and there are no errors.
The next day when powering on a virtual machine, an error is presented:
Not Enough Failover Resources
Which three scenarios are likely causes of this error message? (Choose three.)
A. A host is in maintenance mode for a replacement of a failed Hard Drive.
B. The default slot size in the cluster is set too high.
C. The default VM Monitoring Sensitivity is set too high.
D. There are virtual machines with large CPU reservations.
E. There are not enough datastore heartbeat datastores configured by default.
Answer: A,B,D

NEW QUESTION: 2
You have been given the task to define multiple AWS Data Pipeline schedules for different activities in the
same pipeline. Which of the following would successfully accomplish this task?
A. Defining multiple schedule objects in the schedule field
B. Defining multiple pipeline definitions in your schedule objects file and associating the desired schedule
to the correct activity via its schedule field
C. Creating multiple pipeline definition files
D. Defining multiple schedule objects in your pipeline definition file and associating the desired schedule
to the correct activity via its schedule field
Answer: D
Explanation:
To define multiple schedules for different activities in the same pipeline, in AWS Data Pipeline, you should
define multiple schedule objects in your pipeline definition file and associate the desired schedule to the
correct activity via its schedule field. As an example of this, it could allow you to define a pipeline in which
log files are stored in Amazon S3 each hour to drive generation of an aggregate report once a day.
Reference: https://aws.amazon.com/datapipeline/faqs/

NEW QUESTION: 3
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Large-scale graph algorithms that require one-step link traversal.
B. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
C. Relational operations on large amounts of structured and semi-structured data.
D. Algorithms that require global, sharing states.
E. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
Answer: D
Explanation:
See 3) below. Limitations of Mapreduce - where not to use Mapreduce
While very powerful and applicable to a wide variety of problems, MapReduce is not the answer to every problem. Here are some problems I found where MapReudce is not suited and some papers that address the limitations of MapReuce.
1.Computation depends on previously computed values If the computation of a value depends on previously computed values, then MapReduce cannot be used. One good example is the Fibonacci series where each value is summation of the previous two values. i.e., f(k+2) = f(k+1) + f(k). Also, if the data set is small enough to be computed on a
single machine, then it is better to do it as a single reduce(map(data)) operation rather than going through the entire map reduce process.
2.Full-text indexing or ad hoc searching The index generated in the Map step is one dimensional, and the Reduce step must not generate a large amount of data or there will be a serious performance degradation. For example, CouchDB's MapReduce may not be a good fit for full-text indexing or ad hoc searching. This is a problem better suited for a tool such as Lucene.
3.Algorithms depend on shared global state Solutions to many interesting problems in text processing do not require global synchronization. As a result, they can be expressed naturally in MapReduce, since map and reduce tasks run independently and in isolation. However, there are many examples of algorithms that depend crucially on the existence of shared global state during processing, making them difficult to implement in MapReduce (since the single opportunity for global synchronization in MapReduce is the barrier between the map and reduce phases of processing)
Reference: Limitations of Mapreduce - where not to use Mapreduce

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to It. As a result, these questions will not appear in the review screen.
Your network contains an Active Directory domain named contoso.com. All servers run Windows Server
2016. All client computers run Windows 10.
The relevant objects in the domain are configured as shown in the following table.

You need to assign User1 the right to restore files and folders on Server1 and Server2.
Solution: You create a Group Policy object (GPO), you link the GPO to the Servers OU, and then you modify the Users Rights Assignment in the GPO.
Does this meet the goat?
A. Yes
B. No
Answer: B
Explanation:
Explanation
References:
https://technet.microsoft.com/en-us/library/cc771990(v=ws.11).aspx