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NEW QUESTION: 1
You are administering a multitenant container database (COB) that contains two pluggable databases (PDBs), pdb1 and pdb2. You are connected to pdb2 as a common user with DBA privileges.
The statistics_level parameter is PDB modifiable.
As the user sys, execute the following command on pdb2:
SQL> ALTER SYSTEM SET STATISTICS_LEVEL=ALL SID='*' SCOPE=SPFILE;
Which statement is true about the result of this command?
A. The statistics_level parameter is set to all when the root database is restarted.
B. The statistics_level parameter is set to all when any of the PDBs is reopened.
C. The statement is ignored because there is no SPFILE for a PDB.
D. The statistics_level parameter is set to all only for PDB2 when it is reopened.
Answer: D
Explanation:
Explanation/Reference:
Explanation:

NEW QUESTION: 2
Cloud drive backup does not support batch backup of cloud drives.
A. True
B. False
Answer: B

NEW QUESTION: 3
CSVファイルからテキストを前処理する予定です。 Azure Machine Learning Studioのデフォルトのストップワードリストをロードします。
次の要件を満たすように、テキストの前処理モジュールを構成する必要があります。
*単一の標準形式から複数の関連する単語を確認します。
*テキストからパイプ文字を削除します。
*情報検索を最適化するために単語を削除します。
どの3つのオプションを選択する必要がありますか？回答するには、回答エリアで適切なオプションを選択します。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
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.

NEW QUESTION: 4
Refer to the exhibit.

Two Aruba Mobility Controllers (MC) are managed by a Mobility Master (MM) in a non-cluster deployment as shown in the exhibit.
The C1 controller is connected to AP1 and AP2.
The C2 controller is connected to AP3 and AP4.
VLAN mobility is enabled in the VAPs of both AP Groups.
Both C1 and C2 support the same VLANs.
User1's wireless device supports BSS Fast Transitioning and is currently connected to AP1.
What happens if User1 roams across the network and the user's device associates to AP3?
A. User1's traffic is tunneled to the C1 controller, where they will not have to re-authenticate and can keep their original IP address.
B. User1's traffic is terminated on the C2 controller, and they will have to re-authenticate and can keep their original IP address.
C. User1's traffic is terminated on the C2 controller, and they will not have to re-authenticate but will need to obtain a new IP address.
D. User1's traffic is terminated on the C2 controller, and they will have to re-authenticate and re-obtain a new IP address.
Answer: A