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NEW QUESTION: 1
If a new validation rule is created and there is data already stored that violates the rule, when will Sales force catch the problem?
A. When the record is edited, then saved
B. Immediately
C. When the record is edited
D. When the record is first viewed
E. The record is not affected because it was created before the creation of the validation rule.
Answer: A
Explanation:
When the record is edited, then saved.
Validation rules are enforced when a record is in edit mode, but the validation rules don't
fire until the record is finally saved by the end user.

NEW QUESTION: 2
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、指定された目標を達成できる独自のソリューションが含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答すると、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
20台の仮想マシンをホストするオンプレミスのHyper-Vクラスターがあります。一部の仮想マシンはWindows Server 2016を実行し、一部はLinuxを実行します。
仮想マシンをAzureサブスクリプションに移行する予定です。
仮想マシンのディスクをAzureに複製するソリューションを推奨する必要があります。ソリューションでは、ディスクの移行中も仮想マシンを引き続き使用できるようにする必要があります。
解決策：Azureストレージアカウントを実装してから、Azure Migrateを使用することをお勧めします。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
To ensure that the virtual machines remain available during the migration, use Azure Site Recovery.
Reference:
https://docs.microsoft.com/en-us/azure/site-recovery/site-recovery-overview

NEW QUESTION: 3
TCPとUDPについて正しいものは次のうちどれですか？
Answer:
Explanation:
TCPのみが送信パケットを順序付けします

NEW QUESTION: 4
Case Study: 2 - TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment

TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
- Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
- Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
- Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs.
What should they do?
A. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
B. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
C. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
D. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
Answer: D
Explanation:
Answer should be B - Why would you collect data in Coldline when the purpose is to collect data to analysis down the line and cost wise it should be similar to store such large volume in BQ instead of COldline given the Data access cost involved. ML can be directly done on BQ instead of pulling this data from Coldline into another DB and then applying ML algos on the data which will be more expensive to achieve