70-776試験の独学で勉強者としてどうすればいいですか？
独学で合格するためには、PassexamのMCSA: Data Engineering with Azure資格70-776問題を解くことを中心として勉強をすることがポイントになります。
普段パソコンを使っている人であれば、弊社のMCSA: Data Engineering with Azure資格70-776問題集を勉強すれば2週間程度の独学で合格することができる難易度です。
弊社のMCSA: Data Engineering with Azure資格70-776参考書は本番の試験と同じ問題が出題されます。
弊社のMCSA: Data Engineering with Azure資格70-776学習材料はチェックシステムにて、理解の定着を効果的に確認することができます。
弊社のMCSA: Data Engineering with Azure資格70-776問題集はきちんと理解して本当の学習をしたほうがいいです
70-776試験概要：
試験名称：Microsoft クラウドサービス(ベータ)上でのビッグデータエンジニアリング実施
配信開始: 2017年7月5日
言語: 英語
対象者:データエンジニア
テクノロジ: Microsoft Azure SQLデータウェアハウス, Azureデータレイク分析, Azureデータファクトリー, Azure Stream 分析
対応資格: MCSA
受験料：¥21,103.00 JPY
注 70-776試験は現在ベータ版です。合格すると、該当する認定の完全な認定資格が得られますが、ベータ期間の終了後 8 ～ 12 週間までは、スコア レポートまたは合格/不合格通知を受けることができません。
70-776受験対象者：
MCSA: Data Engineering with Azure資格70-776認定試験は、Azure上で操作済みソリューションを設計し、運用ソリューションを構築する候補者を対象としています。Azure SQL Data Warehouse, Azure Data Lake, Azure Data Factory, Azure Stream Analyticsでのデータエンジニアリング問題に関連する仕事の経験を持つことが、受験者には期待されています
1回の70-776受験で合格を目指すのであれば、PassexamのMCSA: Data Engineering with Azure資格70-776テキストまで理解しておく必要があると思います。
弊社のMCSA: Data Engineering with Azure資格70-776学習資料を購入したら、最も重要な試験準備のことを実現できます。
70-776試験の出題範囲には、以下のトピックが含まれます。
Azure Stream分析を使用して、複雑なイベント処理をデザインし実装(15-20%)
Azure Data Lakeを使用しての分析を設計と実装 (25-30%)
Azure SQL データウェアハウスソリューションの設計と実装(15-20%)
Azure Data Factoryを使用してのクラウドベースの結合を設計と実装 (15-20%)
Azure SQLデータウェアハウス、Azure Data Lake, Azure Data Factory, Azure Stream Analytics の管理とメンテナンス(20-25%)
弊社のMCSA: Data Engineering with Azure資格70-776試験材料を勉強することだけで楽に試験に合格することができます。
弊社のMCSA: Data Engineering with Azure資格70-776学習教材がどんな問題があっても、あるいは君の試験を失敗したら、私たちは全額返金するのを保証いたします。
弊社のMCSA: Data Engineering with Azure資格70-776勉強資料を自力で独学で勉強し合格した時と比較し非常に好成績を取得できます。
弊社のMCSA: Data Engineering with Azure資格70-776勉強資料はPDF版とソフト版を提供しますので、どこでも勉強してもいいです。
購入する前に、問題集の無料な70-776サンプルをダウンロードして試用してもいいです。
1.Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
You are monitoring user queries to a Microsoft Azure SQL data warehouse that has six compute nodes.
You discover that compute node utilization is uneven. The rows_processed column from
sys.dm_pdw_dms_workers shows a significant variation in the number of rows being moved among the
distributions for the same table for the same query.
You need to ensure that the load is distributed evenly across the compute nodes.
Solution: You add a clustered columnstore index.
Does this meet the goal?
A. Yes
B. No
Answer: B
2.Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
You are monitoring user queries to a Microsoft Azure SQL data warehouse that has six compute nodes.
You discover that compute node utilization is uneven. The rows_processed column from
sys.dm_pdw_dms_workers shows a significant variation in the number of rows being moved among the
distributions for the same table for the same query.
You need to ensure that the load is distributed evenly across the compute nodes.
Solution: You add a nonclustered columnstore index.
Does this meet the goal?
A. Yes
B. No
Answer: A
3.Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
You are monitoring user queries to a Microsoft Azure SQL data warehouse that has six compute nodes.
You discover that compute node utilization is uneven. The rows_processed column from
sys.dm_pdw_dms_workers shows a significant variation in the number of rows being moved among the
distributions for the same table for the same query.
You need to ensure that the load is distributed evenly across the compute nodes.
Solution: You change the table to use a column that is not skewed for hash distribution.
Does this meet the goal?
A. Yes
B. No
Answer: A
4.Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You have a table named Table1 that contains 3 billion rows. Table1 contains data from the last 36 months.
At the end of every month, the oldest month of data is removed based on a column named DateTime.
You need to minimize how long it takes to remove the oldest month of data.
Solution: You specify DataTime as the hash distribution column.
Does this meet the goal?
A. Yes
B. No
Answer: A
5.Note: This question is part of a series of questions that present the same scenario. Each question in the
series contains a unique solution that might meet the stated goals. Some question sets might have more
than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these
questions will not appear in the review screen.
You have a table named Table1 that contains 3 billion rows. Table1 contains data from the last 36 months.
At the end of every month, the oldest month of data is removed based on a column named DateTime.
You need to minimize how long it takes to remove the oldest month of data.
Solution: You implement a columnstore index on the DateTime column.
Does this meet the goal?
A. Yes
B. No
Answer: B