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NEW QUESTION: 1
Drag and Drop Question
You are developing a new page for a website that uses Azure Cosmos DB for data storage. The feature uses documents that have the following format:

You must display data for the new page in a specific order. You create the following query for the page:

You need to configure a Cosmos DB policy to the support the query.
How should you configure the policy? To answer, drag the appropriate JSON segments to the correct locations. Each JSON segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation:
Box 1: compositeIndexes
You can order by multiple properties. A query that orders by multiple properties requires a composite index.
Box 2: descending
Example: Composite index defined for (name ASC, age ASC):
It is optional to specify the order. If not specified, the order is ascending.
{
"automatic":true,
"indexingMode":"Consistent",
"includedPaths":[
{
"path":"/*"
}
],
"excludedPaths":[],
"compositeIndexes":[
[
{
"path":"/name",
},
{
"path":"/age",
}
]
]
}

NEW QUESTION: 2
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows

each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems

both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
A. The number of workers
B. The zone
C. The maximum number of workers
D. The disk size per worker
Answer: B

NEW QUESTION: 3
Refer to the exhibit.

Which three NTP features can be deduced on the router (Choose three.)
A. only is in stratum 4
B. only updates its time from 192.168.1.4
C. only accepts time requests from 192.168.1.1
D. only updates its time from 192.168.1.1
E. only handle four requests at a time
F. only accepts time requests from 192.168.1.4
Answer: A,B,C

NEW QUESTION: 4
100個のStandard_F2s_v2 Azure仮想マシンがあります。 各仮想マシンには2つのネットワークアダプターがあります。
仮想マシンで実行されているワークロードのネットワークパフォーマンスを向上させる必要があります。 ソリューションは次の要件を満たしている必要があります。
* CPUとメモリの比率は同じままにする必要があります。
*ソリューションはコストを最小限に抑える必要があります。
あなたは何をするべきか？
A. RDMA over InfiniBandを有効にする
B. 高速ネットワークを有効にします。
C. 追加のネットワークアダプターをインストールする
D. NICチーミングを構成する
Answer: B
Explanation:
Explanation
Accelerated networking enables single root I/O virtualization (SR-IOV) to a VM, greatly improving its networking performance. This high-performance path bypasses the host from the datapath, reducing latency, jitter, and CPU utilization, for use with the most demanding network workloads on supported VM types.
Reference:
https://docs.microsoft.com/en-us/azure/virtual-network/create-vm-accelerated-networking-cli

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