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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question on this series.
You have a database that tracks orders and deliveries for customers in North America. System versioning is enabled for all tables. The database contains the Sales.Customers, Application.Cities, and Sales.CustomerCategories tables.
Details for the Sales.Customers table are shown in the following table:

Details for the Application.Cities table are shown in the following table:

Details for the Sales.CustomerCategories table are shown in the following table:

You are preparing a promotional mailing. The mailing must only be sent to customers in good standing that live in medium and large cities.
You need to write a query that returns all customers that are not on credit hold who live in cities with a population greater than 10,000.
How should you complete the Transact-SQL statement? To answer, drag the appropriate Transact-SQL segments to the correct locations. Each Transact-SQL segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

Answer:
Explanation:

Explanation

Box 1: IN (
The IN clause determines whether a specified value matches any value in a subquery or a list.
Syntax: test_expression [ NOT ] IN ( subquery | expression [ ,...n ] )
Where subquery
Is a subquery that has a result set of one column. This column must have the same data type as test_expression.
Box 2: WHERE
Box 3: AND [IsOnCreditHold] = 0
Box 4: )
References: https://msdn.microsoft.com/en-us/library/ms177682.aspx

NEW QUESTION: 2
CiscoISEノードはどのペルソナを想定できますか。
A. 管理、ポリシーサービス、ゲートキーピング
B. 管理、ポリシーサービス、および監視
C. 管理、監視、およびゲートキーピング
D. ポリシーサービス、ゲートキーピング、および監視
Answer: B
Explanation:
Explanation
https://www.cisco.com/en/US/docs/security/ise/1.0/user_guide/ise10_dis_deploy.html The persona or personas of a node determine the services provided by a node. An ISE node can assume any or all of the following personas: Administration, Policy Service, and Monitoring. The menu options that are available through the administrative user interface are dependent on the role and personas that an ISE node assumes. See Cisco ISE Nodes and Available Menu Options for more information.

NEW QUESTION: 3
製造会社は、機械設備の予知保全を実装したいと考えています。
同社は、AWSにリアルタイムでデータを送信する何千ものloTセンサーをインストールします。
ソリューションアーキテクトは、機械資産ごとに順序付けられた方法でイベントを受信し、後でさらに処理するためにデータが確実に保存されるソリューションを実装する必要があります。
どのソリューションが最も効率的でしょうか？
A. リアルタイムイベントにはAmazon SQS標準キューを使用し、機器資産ごとに1つのキューを使用します。
SQSキューからAWSLambda関数をトリガーして、AmazonS3にデータを保存します。
B. 機器資産ごとに1つのキューを使用して、リアルタイムイベントにAmazon SQSFIFOキューを使用します。
SQSキューのAWSLambda関数をトリガーして、AmazonEFSにデータを保存します。
C. 各機器アセットのシャードを使用したリアルタイムイベントにAmazon Kinesis DataStreamsを使用します。
Amazon Kinesis Data Firehoseを使用して、AmazonEBSにデータを保存します。
D. 各機器アセットのパーティションを使用してリアルタイムイベントにAmazon Kinesis DataStreamsを使用します。
Amazon Kinesis Data Firehoseを使用して、AmazonS3にデータを保存します。
Answer: D
Explanation:
Amazon Kinesis Data Streams collect and process data in real time. A Kinesis data stream is a set of shards. Each shard has a sequence of data records. Each data record has a sequence number that is assigned by Kinesis Data Streams. A shard is a uniquely identified sequence of data records in a stream.
A partition key is used to group data by shard within a stream. Kinesis Data Streams segregates the data records belonging to a stream into multiple shards. It uses the partition key that is associated with each data record to determine which shard a given data record belongs to.

For this scenario, the solutions architect can use a partition key for each device. This will ensure the records for that device are grouped by shard and the shard will ensure ordering. Amazon S3 is a valid destination for saving the data records.
CORRECT: "Use Amazon Kinesis Data Streams for real-time events with a partition key for each device. Use Amazon Kinesis Data Firehose to save data to Amazon S3" is the correct answer.
INCORRECT: "Use Amazon Kinesis Data Streams for real-time events with a shard for each device. Use Amazon Kinesis Data Firehose to save data to Amazon EBS" is incorrect as you cannot save data to EBS from Kinesis.
INCORRECT: "Use an Amazon SQS FIFO queue for real-time events with one queue for each device. Trigger an AWS Lambda function for the SQS queue to save data to Amazon EFS" is incorrect as SQS is not the most efficient service for streaming, real time data.
INCORRECT: "Use an Amazon SQS standard queue for real-time events with one queue for each device. Trigger an AWS Lambda function from the SQS queue to save data to Amazon S3" is incorrect as SQS is not the most efficient service for streaming, real time data.
References:
https://docs.aws.amazon.com/streams/latest/dev/key-concepts.html

NEW QUESTION: 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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:
from azureml.core import Run
import pandas as pd
run = Run.get_context()
data = pd.read_csv('data.csv')
label_vals = data['label'].unique()
# Add code to record metrics here
run.complete()
The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.
You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.
Solution: Replace the comment with the following code:
run.upload_file('outputs/labels.csv', './data.csv')
Does the solution meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.
Note:
Instead use the run_log function to log the contents in label_vals:
for label_val in label_vals:
run.log('Label Values', label_val)
Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai