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NEW QUESTION: 1
Hinweis: Diese Frage ist Teil einer Reihe von Fragen, bei denen die gleichen oder ähnliche Antwortmöglichkeiten verwendet werden. Eine Antwortauswahl kann für mehr als eine Frage in der Reihe richtig sein. Jede Frage ist unabhängig von den anderen Fragen in dieser Reihe. Informationen und Details in einer Frage gelten nur für diese Frage.
Sie migrieren eine Reihe von Datenbanken von einer vorhandenen Microsoft SQL Server-Instanz auf eine neue Instanz.
Sie müssen die Migration abschließen und gleichzeitig den Verwaltungsaufwand und die Ausfallzeiten minimieren.
Welches solltest du implementieren?
A. Ein Windows-Cluster mit einer Shared-Nothing-Architektur
B. Eine SQL Server-Failoverclusterinstanz (FCI)
C. Eine Always On Availability-Gruppe mit sekundären Replikaten im asynchronen Festschreibungsmodus
D. Protokollversand
E. ein Dateifreigabezeuge
F. Eine Always On Availability-Gruppe mit allen Replikaten im Synchron-Commit-Modus
Answer: D
Explanation:
Explanation
SQL Server Log shipping allows you to automatically send transaction log backups from a primary database on a primary server instance to one or more secondary databases on separate secondary server instances. The transaction log backups are applied to each of the secondary databases individually.
References:
https://docs.microsoft.com/en-us/sql/database-engine/log-shipping/about-log-shipping-sql-server?view=sql-serve

NEW QUESTION: 2
You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?
A. Remove entire column
B. Remove entire row
C. Replace with mean
D. Hot Deck
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Remove entire row: Completely removes any row in the dataset that has one or more missing values. This is useful if the missing value can be considered randomly missing.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data Testlet 1 Case study Overview You are a data scientist in a company that provides data science for professional sporting events. Models will use global and local market data to meet the following business goals:
Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.

Assess a user's tendency to respond to an advertisement.

Customize styles of ads served on mobile devices.

Use video to detect penalty events

Current environment
Media used for penalty event detection will be provided by consumer devices. Media may include

images and videos captured during the sporting event and shared using social media. The images and videos will have varying sizes and formats.
The data available for model building comprises of seven years of sporting event media. The sporting

event media includes; recorded video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo

formats.
Penalty detection and sentiment
Data scientists must build an intelligent solution by using multiple machine learning models for penalty

event detection.
Data scientists must build notebooks in a local environment using automatic feature engineering and

model building in machine learning pipelines.
Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation.

Notebooks must execute with the same code on new Spark instances to recode only the source of the

data.
Global penalty detection models must be trained by using dynamic runtime graph computation during

training.
Local penalty detection models must be written by using BrainScript.

Experiments for local crowd sentiment models must combine local penalty detection data.

Crowd sentiment models must identify known sounds such as cheers and known catch phrases.

Individual crowd sentiment models will detect similar sounds.
All shared features for local models are continuous variables.

Shared features must use double precision. Subsequent layers must have aggregate running mean

and standard deviation metrics available.
Advertisements
During the initial weeks in production, the following was observed:
Ad response rated declined.

Drops were not consistent across ad styles.

The distribution of features across training and production data are not consistent

Analysis shows that, of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrelated features.
Initial data discovery shows a wide range of densities of target states in training data used for crowd

sentiment models.
All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are

running too slow.
Audio samples show that the length of a catch phrase varies between 25%-47% depending on region

The performance of the global penalty detection models shows lower variance but higher bias when

comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.
Ad response models must be trained at the beginning of each event and applied during the sporting

event.
Market segmentation models must optimize for similar ad response history.

Sampling must guarantee mutual and collective exclusively between local and global segmentation

models that share the same features.
Local market segmentation models will be applied before determining a user's propensity to respond to

an advertisement.
Ad response models must support non-linear boundaries of features.

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated

from 0.1 +/- 5%.
The ad propensity model uses cost factors shown in the following diagram:


The ad propensity model uses proposed cost factors shown in the following diagram:


Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

NEW QUESTION: 3
以下を正確な説明に一致させる

Answer:
Explanation:

Explanation
Authorization - configuration priviledge levels on device etcChange Management - processes to manage changes in envrionment etcAccess-control - controlled access to devices Operation policy - defined guideline instruction for operators to followSegregated of duty - network group roles, which action to be performed by which group etcAccounting - track changes timelime action

NEW QUESTION: 4
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You maintain a Microsoft SQL Server instance that contains the following databases SalesDb1, SalesDb2, and SalesDb3. Each database has tabled named Products and Sales. The following table shows the configuration of each database.

The backup strategies for each database are described in the following table.

Each full or differential backup operation writes into a new file and uses a different sequence number. You observe the following database corruption issues.

SalesDb3 reports a number of database corruption issues related to error 823 and 824 when reading data pages.
You must display the following information about the corrupted pages:
* database name
* impacted file id
* impacted file physical name
* impacted page id
* event type that identifies the error type
* error count
Users report performance issues when they run queries against SalesDb2. You plan to monitor query statistics and execution plans for SalesDb2 by using Query Store. The monitoring strategy must meet the following requirements:
* Perform automatic data cleanup when query store disk usage reaches 500 megabyte (MB).
* Capture queries based on resource consumption.
* Use a stale query threshold value of 60 days.
The query optimizer generates suboptimal execution plans for a number of queries on the Sales table in SalesDb2. You will create a maintenance plan that updates statistics for the table. The plan should only update statistics that were automatically created and have not been updated for 30 days. The update should be based on all data in the table.
You need to view the information about the corrupted pages on SalesDb3.
How should you complete the Transact-SQL statement?
Select two.
A. WHERE event_type = 3
B. WHERE event_type = 2
C. SELECT * FROM msdb.. corrupted_pages
D. SELECT * FROM system..corrupted_pages
E. SELECT * FROM msdb..suspect_pages
F. SELECT * FROM system..suspect_pages
G. WHERE event_type = 1
Answer: B,E
Explanation:
Explanation
suspect_pages contains one row per page that failed with a minor 823 error or an 824 error. Pages are listed in this table because they are suspected of being bad, but they might actually be fine. When a suspect page is repaired, its status is updated in the event_type column.
The suspect_pages table resides in the msdb database.
SalesDb3 has pages with checksum errors. Checksum errors have the event_type value 2.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/backup-restore/manage-the-suspect-pages-table-sql-ser