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NEW QUESTION: 1
According to NetApp best practices, which two sets of protocols would be assigned to the same storage virtual machine (SVM)? (Choose two.)
A. NFS. CIFS
B. FCP, NFS
C. FCP, iSCSI
D. CIFS, iSCSI
Answer: A
Explanation:
For Storage Virtual Machines (SVMs) that are accessible from multiple protocols, such as CIFS and NFS, you can use DNS load balancing and automatic LIF rebalancing simultaneously.
Reference: Clustered Data ONTAP 8.3, Network Management Guide, page 96

NEW QUESTION: 2
Which three storage components are available after nonvirtualized standard deployment of an X5 Database Machine using high-capacity disks?
A. Exadata Smart Flash Cache using all of the flashdisk space
B. the RECO_<DBM_Name> ASM diskgroup
C. mirrored system area on hard disk 0, hard disk 1, and hard disk 2
D. the DATA_<DBM_Name> ASM diskgroup
E. the DBFS_DG diskgroup with high redundancy
F. mirrored system area on hard disk 0 and hard disk 1
Answer: B,D,E
Explanation:
The first two disks of Exadata Storage Server are system disks. Oracle Exadata Storage Server Software system software resides on a portion of each of the system disks. These portions on both system disks are referred to as the system area.
The default configuration on Exadata is to have 3 diskgroups - DATA, RECO, and DBFS_DG.

NEW QUESTION: 3
開発者は、特定のリクエストがAmazon API Gatewayエンドポイント/ MyAPIを通過するが、リクエストがAWS Lambda関数のバッキングに到達しないという問題を調査しています
/ MyAPI。開発者は、2番目のLambda関数が、特定のAWSアカウントで許可されている最大同時実行で実行される場合があることを発見しました。
開発者はこの問題にどのように対処できますか？
A. 2番目のLambda関数の同時実行制限を構成します
B. アカウントレベルで同時実行制限を手動で引き下げます
C. / MyAPIの別のAPI Gatewayステージを追加し、リクエストを分割します
D. API Gateway / MyAPIエンドポイントのスロットル制限を減らします
Answer: A
Explanation:
https://aws.amazon.com/about-aws/whats-new/2017/11/set-concurrency-limits-on-individual-aws-lambda-functions/
You can now set a concurrency limit on individual AWS Lambda functions. The concurrency limit you set will reserve a portion of your account level concurrency limit for a given function. This feature allows you to throttle a given function if it reaches a maximum number of concurrent executions allowed, which you can choose to set.

NEW QUESTION: 4
Case Study: 6 - TerramEarth
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.
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?
A. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
B. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
C. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
D. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
Answer: D