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NEW QUESTION: 1
Identify the two true statements about incremental loading.
A. Different materialized views may exist within a slice as compared to the main slice of the database.
B. Materialization of slices is required to provide users the correct query results.
C. Allows for real time data access for end users.
D. Creates *subscribes* along the main slice in the database.
Answer: B,C
Explanation:
Explanation: A: Incremental loading and fast aggregation can provide near real-time analysis of transactional data.
C: Incremental loading creates subcubes or slices alongside the primary slice of the database.
Note: Following a data load, Essbase ASO does not store any aggregate values, but instead calculates them on demand. For large databases, where the time required to generate these values may become inconvenient, the database can materialize one or more aggregate "views", made up of one aggregate level from each dimension (for example, the database may calculate all combinations of the fifth generation of Product with the third generation of Customer), and these views are then used to generate other aggregate values where possible. This process can be partially automated, where the administrator specifies the amount of disk space that may be used, and the database generates views according to actual usage.
This approach has a major drawback in that the cube cannot be treated for calculation purposes as a single large hypercube, because aggregate values cannot be directly controlled, so write-back from front-end tools is limited, and complex calculations that cannot be expressed as MDX expressions are not possible.
Hyperion Essbase - System 9 Database Administrator's Guide

NEW QUESTION: 2
組織は大容量のハッシュタグデータのストリームをリアルタイムで処理する必要があり、特定のタグに関する洞察を得るためにデータに対してカスタムSQLクエリを実行する必要があります。組織はこのソリューションに弾力性が必要であり、クラスターを管理したくないと考えています。
これらの要件を満たすAWSサービスは次のうちどれですか？
A. Amazon Kinesis Data Analytics
B. Amazon Elasticsearch Service
C. アマゾンアテナ
D. Amazon Redshift
Answer: A

NEW QUESTION: 3
モデルの適合の問題を修正する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation

Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.