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NEW QUESTION: 1

A. SQLServer:SSIS Pipeline 10.0: Flat butter memory
B. Memory\Pages/sec
C. SQLServer: SSIS Pipeline 10.0:Private buffer memory
D. SQLServer:SSIS Pipeline 10.0: Buffers in use
Answer: C
Explanation:
Explanation
Private buffer performance counters
Instead of being created by an SSIS instance, private buffers are created by particular transformation process themselves. Their purpose is to support and optimize the performance of temporary tasks in Pipeline, like the Aggregation transformation, Sort transformation etc.
Private buffer memory: this performance counter shows the full amount of physical or virtual memory used by all of the current private buffers within the Data Flow engine task in the monitoring cycle. During a data transformation, if the Data flow engine creates a buffer to support itself (not the transformation), that buffer is not considered as private.
References: https://www.sqlshack.com/ssis-pipeline-performance-counters-guide/

NEW QUESTION: 2
ユーザーであるAnnは、企業のWiFiに接続し、インターネットの閲覧を試みます。アンは、ローカル（イントラネット）ページにしかアクセスできないことに気付きます。 MOSTが問題を解決する可能性が高いのは次のアクションのうちどれですか？
A. ポップアップブロッカーを無効にします
B. ブラウザープロキシ設定を構成します。
C. ブラウザーのキャッシュをクリアします。
D. IPアドレスを更新します。
Answer: B

NEW QUESTION: 3
You need to implement a model development strategy to determine a user's tendency to respond to an ad.
Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Split Rows module to partition the data based on centroid distance.
C. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on distance travelled to the event.
Answer: A
Explanation:
Explanation
Split Data partitions the rows of a dataset into two distinct sets.
The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.
Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.
Scenario:
Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.
The distribution of features across training and production data are not consistent References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data
Topic 1, Case Study 1
Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will be 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.
*Access 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
Requirements
* Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared 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 videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.
*Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.
Advertisements
* Ad response models must be trained at the beginning of each event and applied during the sporting event.
* Market segmentation nxxlels must optimize for similar ad resporr.r history.
* Sampling must guarantee mutual and collective exclusivity 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.
* Data scientists must be able to detect model degradation and decay.
* Ad response models must support non linear boundaries features.
* The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates 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:

Penalty detection and sentiment
Findings
*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.
segments
During the initial weeks in production, the following was observed:
*Ad response rates 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 uncorrected features.
Penalty detection and sentiment
*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 stow.
*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 show 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.

NEW QUESTION: 4
What determines which bits in an IP address represent the subnet, and which represent the host?
A. multicast
B. broadcast
C. netmask
D. Subnet
E. unicast
Answer: C
Explanation:
A subnetwork, or subnet, is a logically visible subdivision of an IP network. The practice of dividing a network into two or more networks is called subnetting.
The routing prefix of an address is written in a form identical to that of the address itself.
This is called the network mask, or netmask, of the address. For example, a specification of the most-significant 18 bits of an IPv4 address, 11111111.11111111.11000000.00000000, is written as 255.255.192.0.

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