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NEW QUESTION: 1
For the past year a manufacturing company has recorded the number of units produced (x) each week and the total production cost (y) for that week. The company intends to use this data to predict future costs.
For the circumstance described above, linear regression is more useful and accurate than the high-low method because:
1. It uses all the sets of data observed to calculate the line of best fit.
2. The coefficient of variation can estimate what percentage of x is due to a change in y.
3. Forecasts remain valid for values for x outside of the observed range.
Which of the above statements are true?
A. 1 and 2
B. 3 only
C. 1 only
D. 2 and 3
Answer: C
NEW QUESTION: 2
A bicycle sharing company is developing a multi-tier architecture to track the location of its bicycles during peak operating hours The company wants to use these data points in its existing analytics platform A solutions architect must determine the most viable multi-tier option to support this architecture The data points must be accessible from the REST API Which action meets these requirements for storing and retrieving location data?
A. Use Amazon API Gateway with Amazon Kinesis Data Analytics
B. Use Amazon QuickSight with Amazon Redshift
C. Use Amazon Athena with Amazon S3
D. Use Amazon API Gateway with AWS Lambda
Answer: C
NEW QUESTION: 3
会社は、数ペタバイトのデータセットをクラウドに移行することを計画しています。データセットは24時間利用可能でなければなりません。ビジネスアナリストは、SQLインターフェイスを使用した経験しかありません。分析を容易にするために、データをどのように保存して最適化する必要がありますか？
A. データをGoogle Cloud SQLに挿入します。
B. データをGoogle Cloud Datastoreにストリーミングします。
C. データをGoogle BigQueryにロードします。
D. フラットファイルをGoogle Cloud Storageに配置します。
Answer: C
Explanation:
説明
Google Big Queryは、マルチペタバイトストレージ、HA（高可用性）、つまり24時間、SQLインターフェイス用です
。
https://medium.com/google-cloud/the-12-components-of-google-bigquery-c2b49829a7c7
https://cloud.google.com/solutions/bigquery-data-warehouse
https://cloud.google.com/bigquery/
BigQueryは、すべてのデータアナリストの生産性を高めるように設計された、Googleのサーバーレスで拡張性の高い低コストのエンタープライズデータウェアハウスです。管理するインフラストラクチャがないため、使い慣れたSQLを使用してデータの分析に集中して有意義な洞察を見つけることができ、データベース管理者は必要ありません。
BigQueryを使用すると、オブジェクトストレージやスプレッドシートからのデータだけでなく、管理された円柱状のストレージ上に論理データウェアハウスを作成することにより、すべてのデータを分析できます。
参照：https://cloud.google.com/bigquery/
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