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NEW QUESTION: 1
For Make Items, the time required to make the Item If you have all of the Buy Items In Inventory and have to make all Subassemblies and the Item Itself Is called______.
A. Lead Time Lot Size
B. Cumulative Manufacturing Lead Time
C. Cumulative Total Lead Time
D. Preprocessing Lead Time
E. Fixed Lead Time
F. Processing Lead Time
G. Variable Lead Time
H. Postprocessing Lead Time
Answer: C
NEW QUESTION: 2
Examine the utilization parameters for an instance:
You notice that despite having an index on the column used in the where clause, queries use full table scans with highly selective filters.
What are two possible reasons for the optimizer to use full table scans instead of index unique scans and index range scans?
A. The number of leaf blocks for the indexes is high.
B. The blocks fetched by the query are greater than the value specified by the DB_FILE_MULTIBLOCK_READ_COUNT parameter.
C. The OPTIMIZER_INDEX_COST_ADJ initialization parameter is set to 100.
D. The OPTIMIZER_MODE parameter is set to ALL_ROWS.
E. The clustering factor for the indexes is high.
Answer: B,C
Explanation:
D:OPTIMIZER_INDEX_COST_ADJ lets you tune optimizer behavior for access path selection to be more or less index friendly-that is, to make the optimizer more or less prone to selecting an index access path over a full table scan. The default for this parameter is 100 percent, at which the optimizer evaluates index access paths at the regular cost. Any other value makes the optimizer evaluate the access path at that percentage of the regular cost. For example, a setting of 50 makes the index access path look half as expensive as normal.
E:DB_FILE_MULTIBLOCK_READ_COUNT is one of the parameters you can use to minimize I/O during table scans. It specifies the maximum number of blocks read in one I/O operation during a sequential scan. The total number of I/Os needed to perform a full table scan depends on such factors as the size of the table, the multiblock read count, and whether parallel execution is being utilized for the operation. As of Oracle Database 10g release 2, the default value of this parameter is a value that corresponds to the maximum I/O size that can be performed efficiently. This value is platform-dependent and is 1MB for most platforms.Because the parameter is expressed in blocks, it will be set to a value that is equal to the maximum I/O size that can be performed efficiently divided by the standard block size. Note that if the number of sessions is extremely large the multiblock read count value is decreased to avoid the buffer cache getting flooded with too many table scan buffers. Even though the default value may be a large value, the optimizer will not favor large plans if you do not set this parameter. It would do so only if you explicitly set this parameter to a large value. Online transaction processing (OLTP) and batch environments typically have values in the range of 4 to 16 for this parameter. DSS and data warehouse environments tend to benefit most from maximizing the value of this parameter. The optimizer is more likely to choose a full table scan over an index if the value of this parameter is high.
Note:
*OPTIMIZER_MODE establishes the default behavior for choosing an optimization approach for the instance.
Values: first_rows_n
The optimizer uses a cost-based approach and optimizes with a goal of best response time to return the first n rows (where n = 1, 10, 100, 1000). first_rows The optimizer uses a mix of costs and heuristics to find a best plan for fast delivery of the
first few rows. all_rows The optimizer uses a cost-based approach for all SQL statements in the session and
optimizes with a goal of best throughput (minimum resource use to complete the entire statement).
NEW QUESTION: 3
ケーススタディ：6 - TerramEarth
会社概要
TerramEarthは、鉱業および農業業界向けの重機を製造しています。約彼らの事業の80％は鉱業から、そして20％は農業からのものです。現在、100カ国に500以上のディーラーとサービスセンターがあります。彼らの使命は、顧客をより生産的にする製品を作ることです。
ソリューションコンセプト
1秒間に120フィールドのデータを収集する2,000万台のTerramEarth車両が稼働しています。
データは車両にローカルに保存され、車両の修理時に分析のためにアクセスできます。
データはメンテナンスポートを介してダウンロードされます。この同じポートを使用して動作パラメータを調整し、新しいコンピューティングモジュールで現場での車両のアップグレードを可能にすることができます。
約200,000台の車両が携帯電話ネットワークに接続されているため、TerramEarthはデータを直接収集できます。 1日あたり22時間の運用で1秒あたり120フィールドのデータレートで、TerramEarthはこれらの接続された車両から合計約9 TB /日を収集します。
既存の技術環境
TerramEarthの既存のアーキテクチャは、単一の米国西海岸ベースのデータセンターに常駐するLinuxおよびWindowsベースのシステムで構成されています。これらのシステムは現場からCSVファイルをgzipしてFTPでアップロードし、データをデータウェアハウスに保存します。このプロセスには時間がかかるため、集約レポートは3週間前のデータに基づいています。
このデータを使用して、TerramEarthは交換部品の優先在庫を確保し、計画外のダウンタイムを60％削減することができました。しかし、データが古くなっているため、一部の顧客は交換部品を待つ間、最長4週間車を購入できません。
ビジネス要件
計画外の車両のダウンタイムを1週間以内に減らします。
* 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
エグゼクティブステートメント
当社の競争上の優位性は常に製造プロセスにあり、競合他社よりも低コストでより良い車両を製造することができます。 しかし、さまざまなアプローチを持つ新製品が絶えず開発されており、私たちの業界で次の変革の波を経験するためのスキルが不足していることを私は心配しています。 私の目標は、段階的な技術革新を通じて、当面の市場ニーズに対応しながら、私たちのスキルを磨くことです。
この質問については、TerramEarthのケーススタディを参照してください。 携帯電話ネットワークに接続されている20万台の車両のデータを取り込むための新しいアーキテクチャを設計するように求められます。 あなたはグーグルが推奨する慣習に従いたい。
技術的要件を考慮して、データの取り込みにどのコンポーネントを使用するべきですか？
A. SSL Kgressを備えたGoogle Kubernetesエンジン
B. プロジェクト全体のSSHキーを持つCompute Engine
C. 公開/秘密鍵ペアを使用したクラウドIoTコア
D. 特定のSSHキーを持つCompute Engine
Answer: A