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NEW QUESTION: 1
You plan to migrate a database supporting both DSS and OLTP workloads to your new X5 Database machine.
The workloads contain many complex aggregating functions and expensive joins on large partitioned tables in the DSS workload and indexed access for OLTP workloads.
Which three benefits accrue as a result of this migration?
A. Cell offload processing for indexed-organized table access
B. Superior compression capability designed specifically for data warehouse tables
C. Superior flash cache compression technique
D. Columnar storage capability for data in row major data blocks that is held in flash cache
E. Superior compression capability designed specifically for OLTP workloads
F. Superior compression capability designed specifically for archival data
Answer: B,E,F
Explanation:
Explanation/Reference:
There are 6 different kinds of Table Data Compression methods:
1. BASIC compression, introduced in Oracle 8 already and only recommended for Data Warehouse
2. OLTP compression, introduced in Oracle 11 and recommended for OLTP Databases as well
3. QUERY LOW compression (Exadata only), recommended for Data Warehouse with Load Time as a critical factor
4. QUERY HIGH compression (Exadata only), recommended for Data Warehouse with focus on Space Saving
5. ARCHIVE LOW compression (Exadata only), recommended for Archival Data with Load Time as a critical factor
6. ARCHIVE HIGH compression (Exadata only), recommended for Archival Data with maximum Space Saving
Incorrect Answers:
B:
1. BASIC compression, introduced in Oracle 8 already and only recommended for Data Warehouse
2. OLTP compression, introduced in Oracle 11 and recommended for OLTP Databases as well
3. QUERY LOW compression (Exadata only), recommended for Data Warehouse with Load Time as a critical factor
4. QUERY HIGH compression (Exadata only), recommended for Data Warehouse with focus on Space Saving
5. ARCHIVE LOW compression (Exadata only), recommended for Archival Data with Load Time as a critical factor
6. ARCHIVE HIGH compression (Exadata only), recommended for Archival Data with maximum Space Saving
Method 1 and 2 are working by compressing the rows inside of Oracle Blocks - the more redundant values inside the block, the better the compression ratio. OLTP compression might be used with Exadata Database Machine, because Exadata V2 is designed to support OLTP as well as Data Warehouse.
Methods 3 to 6 use Hybrid Columnar Compression (HCC), which is designed to achieve better compression ratios by organizing sets of rows into compression units and then detaching the columns from the rows and collecting them. It is more likely that the same columns store similar (redundant) data, therefore the compression ratio improves with HCC.
References: https://uhesse.com/2011/01/21/exadata-part-iii-compression/

NEW QUESTION: 2
Which two options are supported on Cisco UCS Fabric Interconnects that use native Fibre Channel connectivity? (Choose two)
A. DAS
B. NFS
C. iSCSI
D. iFCP
E. SAN
Answer: B,C

NEW QUESTION: 3
Azure Stream Analyticsジョブを監視しています。
バックログ入力イベントメトリックがゆっくりと増加し、常にゼロ以外であることがわかります。
ジョブがすべてのイベントを処理できることを確認する必要があります。
あなたは何をするべきか？
A. 接続から名前付きコンシューマグループを削除し、$ defaultを使用します。
B. 既存の入力ストリーム用に追加の出力ストリームを作成します。
C. Stream Analyticsジョブの互換性レベルを変更します。
D. ストリーミングユニット（SU）の数を増やします。
Answer: D
Explanation:
Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job. You should increase the Streaming Units.
Note: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.
Reference:
https://docs.microsoft.com/bs-cyrl-ba/azure/stream-analytics/stream-analytics-monitoring