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NEW QUESTION: 1
View the Exhibit and examine the structure of the ORDERS table.
You have to display ORDER_ID, ORDER_DATE, and CUSTOMER_ID for all those orders that were placed after the last order placed by the customer whose CUSTOMER_ID is 101 Which query would give you the desired output?
A. SELECT order_id, order_date FROM orders
WHERE order_date > ANY (SELECT order_date
FROM orders
WHERE customer_id = 101);
B. SELECT order_id, order_date FROM orders
WHERE order_date > ALL (SELECT order_date
FROM orders
WHERE customer_id = 101);
C. SELECT order_id, order_date FROM orders
WHERE order_date > ALL (SELECT MAX(order_date) FROM orders) AND
Customer_id = 101;
D. SELECT order_id, order_date FROM orders
WHERE order_date IN (SELECT order_date FROM orders
WHERE customer id = 101);
Answer: B
NEW QUESTION: 2
Drag and drop the descriptions on the left to the correct operational planes on the right.
Answer:
Explanation:
Explanation
NEW QUESTION: 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.
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 create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:
* /data/2018/Q1 .csv
* /data/2018/Q2.csv
* /data/2018/Q3.csv
* /data/2018/Q4.csv
* /data/2019/Q1.csv
All files store data in the following format:
id,f1,f2,l
1,1,2,0
2,1,1,1
3.2.1.0
You run the following code:
You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:
Solution: Run the following code:
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Use two file paths.
Use Dataset.Tabular_from_delimeted as the data isn't cleansed.
Note:
A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets