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NEW QUESTION: 1
情報セキュリティ戦略の最も重要な要素は次のうちどれですか？
A. 配達の時間枠
B. 定義された目標
C. 制御フレームワークの採用
D. 完全なポリシー
Answer: B
Explanation:
説明
定義された目標がなければ、戦略-目標を達成するための計画-は開発できません。配信の時間枠は重要ですが、戦略文書に含めるためには重要ではありません。同様に、制御フレームワークの採用は、情報セキュリティ戦略を成功させるために重要ではありません。ポリシーは、戦略の実装に続いてその一部として開発されます。

NEW QUESTION: 2
You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.
Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.
You need to produce the distribution.
Which type of distribution should you produce?
A. Unpaired t-test with a two tail option
B. Paired t-test with a one-tail option
C. Unpaired t-test with a one-tail option
D. Paired t-test with a two-tail option
Answer: D
Explanation:
Explanation
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test
https://en.wikipedia.org/wiki/Student%27s_t-test
Topic 1, Case Study 2
Case study
Overview
You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Datasets
There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:

The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column.
The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
Model fit
The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Experiment requirements
You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.
In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.
You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
Data visualization
You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

NEW QUESTION: 3
On your Oracle Database, you issue the following commands to create indexes:
SQL > CREATE INDEX oe.ord_customer_ix1 ON oe.orders (customer_id, sales_rep_id) INVISIBLE;
SQL> CREATE BITMAP INDEX oe.ord_customer_ix2 ON oe.orders (customer_id, sales_rep_id);
Which two statements are true?
A. Both the indexes are created and used by the optimizer for queries on the ORDERS table.
B. Both the indexes are created: however, only ORD_CUSTOMERS_IX2 is used by the optimizer for queries on the ORDERS table.
C. Both the indexes are updated when a row is inserted, updated, or deleted in the ORDERS table.
D. Only the ORD_CUSTOMER_IX1 index created.
E. Both the indexes are created: however, only ORD_CUSTOMERS_IX1 is used by the optimizer for queries on the ORDERS table.
F. The ORD_CUSTOMER_IX1 index is not used by the optimizer even when the OPTIMIZER_USE_INVISIBLE_INDEXES parameters is set to true.
Answer: B,C
Explanation:
Not A: Both indexes are created fine.
B: The invisible index ORD_CUSTOMERS_IX1 and the bitmap index are both updated by DML operations on the Orders table.
F: Since ORD_CUSTOMERS_IX1 is invisible only ORD_CUSTOMERS_IX2 is used by the query optimizer.
Not C,Not D,Not E:
* ord_customer_ix1 is an invisible index and is therefore not used by the optimizer.
* VISIBLE | INVISIBLE Use this clause to specify whether the index is visible or invisible to the optimizer. An invisible index is maintained by DML operations, but it is not be used by the optimizer during queries unless you explicitly set the parameter OPTIMIZER_USE_INVISIBLE_INDEXES to TRUE at the session or system level.
Note: Specify BITMAP to indicate that index is to be created with a bitmap for each distinct key, rather than indexing each row separately. Bitmap indexes store the rowids associated with a key value as a bitmap. Each bit in the bitmap corresponds to a possible rowid. If the bit is set, then it means that the row with the corresponding rowid contains the key value. The internal representation of bitmaps is best suited for applications with low levels of concurrent transactions, such as data warehousing.

NEW QUESTION: 4
You need to enable the digit 8 to be used as the AAR access code.
Which SAT command would you use to define the digit 8 as the AAR access code?
A. change aar analysis
B. change feature-access-code
C. change aar parameters
D. add aar parameters
Answer: B

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