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NEW QUESTION: 1
Your organization leverages an IP Address Management (IPAM) product to manage IP address distribution. The IPAM exposes an API. Development teams use CloudFormation to provision approved reference architectures. At deployment time, IP addresses must be allocated to the VPC. When the VPC is deleted, the IPAM must reclaim the VPC's IP allocation.
Which method allows for efficient, automated integration of the IPAM with CloudFormation?
A. AWS CloudFormation parameters using the "Ref::" intrinsic function
B. AWS CloudFormation parameters using the "Fn::FindInMap" intrinsic function.
C. AWS CloudFormation custom resource using an AWS Lambda invocation.
D. CloudFormation::OpsWorks::Stack with custom Chef configuration.
Answer: C
Explanation:
When a custom resource is created, updated, or deleted, AWS CloudFormation sends a notification to an Amazon Simple Notification Service (Amazon SNS) topic or invokes an AWS Lambda function.
NEW QUESTION: 2
The following statements are about health plans' complaint resolution procedures (CRPs). Three of the statements are true and one is false. Select the answer choice containing the FALSE statement.
A. CRPs include only formal appeals and do not apply to informal complaints.
B. An health plan's CRPs reduce the likelihood of errors in decision making.
C. Most complaints are resolved without proceeding through the entire CRP process.
D. CRPs typically provide for at least two levels of appeal for formal appeals.
Answer: A
NEW QUESTION: 3
100個のユーザーアカウントを含むcontoso.onmicrosoft.comという名前のAzure Active Directory（Azure AD）テナントがあります。
テナント用に10個のAzure AD Premium P2ライセンスを購入します。
10人のユーザーがすべてのAzure AD Premium機能を使用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. 各ユーザーの[グループ]ブレードから、ユーザーをグループに招待します。
B. 各ユーザーのディレクトリロールブレードから、ディレクトリロールを変更します。
C. Azure ADドメインから、エンタープライズアプリケーションを追加します。
D. Azure ADの[ライセンス]ブレードから、ライセンスを割り当てます。
Answer: D
Explanation:
To assign a license, under Azure Active Directory > Licenses > All Products, select one or more products, and then select Assign on the command bar.
References: https://docs.microsoft.com/en-us/azure/active-directory/fundamentals/license-users-groups
NEW QUESTION: 4
You have a feature set containing the following numerical features: X, Y, and Z.
The Poisson correlation coefficient (r-value) of X, Y, and Z features is shown in the following image:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: 0.859122
Box 2: a positively linear relationship
+1 indicates a strong positive linear relationship
-1 indicates a strong negative linear correlation
0 denotes no linear relationship between the two variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation
Topic 1, Case Study
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.
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