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NEW QUESTION: 1
The sales table has columns prod_id and quantity_sold of data type number. Which two queries execute successfully?
A. SELECT COUNT(prod_id) FROM sales WHERE quantity_sold > 55000 GROUP BY prod_id;
B. SELECT COUNT(prod_id) FROM sales GROUP BY prod_id WHERE quantity_sold > 55000;
C. SELECT prod Id FROH sales NHERE quantity sold > 55000 3RODI BY prod_id HAVING COUNT(*)
> 10;
D. SELECT prod_id FROM sales WHERE quantity_sold > 55000 AND
COUNT(*) > 10 GROUP BY COUNT(*) > 10;
E. SELECT prod_id FROM sales WHERE quantity_sold > 55000 AND COUNT(*) > 10 GROUP BY prod_id HAVING COUNT(*) > 10;
Answer: A,C

NEW QUESTION: 2
You are designing a SQL Server Integration Services (SSIS) 2016 package that imports data from a Windows Azure SQL Database database into a SQL Server database.
The SSIS package has the following requirements:
* Every night, a very large amount of data is imported into the staging database.
* Package processing time must be minimized.
* The package must run on its own dedicated server when it is deployed to production.
* Transaction log activity when data is imported must be minimized.
You need to design the package to meet the requirements.
Which destination component should you use?
A. ODBC
B. Bulk Insert
C. OLE DB
D. Raw File
Answer: C

NEW QUESTION: 3
PyTorchフレームワークを使用して、マルチクラス画像分類の深層学習実験を作成します。 GPUを備えたノードを持つAzure Computeクラスターで実験を実行する予定です。
画像分類モデルの毎月の再トレーニングを実行するには、Azure Machine Learningサービスパイプラインを定義する必要があります。パイプラインは最小限のコストで実行し、モデルのトレーニングに必要な時間を最小限に抑える必要があります。
どの3つのパイプラインステップを順番に実行する必要がありますか？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領域に移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation

Step 1: Configure a DataTransferStep() to fetch new image data...
Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target.
Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target.
The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-pytorch

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