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NEW QUESTION: 1
A system administrator has created a Jython script called globalScript.py.
What should the administrator do to ensure globalScript.pyis loaded when the wsadminshell is used?
A. Set the script profiles in the wsadmin.propertiesfile to load globalScript.py.
B. Invoke wsadminwith the argument -profileName globalScript.py.
C. Modify the configureCustomPropertyscript to import globalScript.py.
D. Compile globalScript.pyto a Java class in the bin directory.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
wsadmin.properties has this entry com.ibm.ws.scripting.profiles where we can add the scripting files to be loaded.
References: https://www-01.ibm.com/support/knowledgecenter/SSAW57_8.5.5/ com.ibm.websphere.nd.doc/ae/rxml_propscript.html

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する可能性のあ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した後は、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。
ABAPベースのシステムとJavaベースのシステムの両方を備えた複雑なSAP環境があります。現在のオンプレミスランドスケープは、Windows ServerおよびMicrosoft SQL Serverで実行されているSAP NetWeaver 7.0（UnicodeおよびNon-Unicode）に基づいています。
SAP環境をHANA認定のAzure環境に移行する必要があります。
ソリューション：Azure Site Recoveryを使用してSAPをAzureに移行してから、SAP NetWeaver 7.4にアップグレードします。
これは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Explanation
We need upgrade to SAP NetWeaver 7.4 before the migration.
Reference:
https://docs.microsoft.com/en-us/azure/site-recovery/vmware-azure-architecture

NEW QUESTION: 3
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase
their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating
efficiency of all 20 million cellular and unconnected vehicles in the field.
How can you accomplish this goal?
A. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud
Messaging (GCM) to make operational adjustments automatically
B. Capture all operating data, train machine learning models that identify ideal operations, and host in
Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically
C. Have you engineers inspect the data for patterns, and then create an algorithm with rules that make
operational adjustments automatically
D. Capture all operating data, train machine learning models that identify ideal operations, and run locally
to make operational adjustments automatically
Answer: B
Explanation:
Explanation/Reference:
References: https://cloud.google.com/customers/ocado/
Testlet 1
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.
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 and Windows-based systems that reside in a
single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP,
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.

Support the dealer network with more data on how their customers use their equipment to 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.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east coast.

Create a backup strategy.

Increase security of data transfer from equipment to the datacenter.

Improve data in the data warehouse.

Use customer and equipment data to anticipate customer needs.

Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2

- 16 CPUs
- 128 GB of RAM
- 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs
repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application
at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs

- Windows Server 2008 R2
- 16 CPUs
- 32 GB of RAM
- 500 GB HDD
Data warehouse:
A single PostgreSQL server

- RedHat Linux
- 64 CPUs
- 128 GB of RAM
- 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better
vehicles for lower 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. My goals are to build our skills while addressing immediate market needs
through incremental innovations.

NEW QUESTION: 4
ソリューションアーキテクトは、Amazon LinuxおよびWindowsを実行しているすべてのAmazon EC2インスタンスのメモリおよびディスクスペースの使用率を監視できるソリューションを設計しています。
この要件を満たすソリューションはどれですか？
A. Amazon Inspector resource monitoring.
B. Custom Amazon CloudWatch metrics.
C. Default Amazon CloudWatch metrics.
D. Default monitoring of Amazon EC2 instances.
Answer: B
Explanation:
Explanation
https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/mon-scripts.html