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NEW QUESTION: 1
有効性をチェックしない非検証XMLプロセッサを使用して以下の属性値を読み取った結果を正しく説明しているステートメントを選択してください。 [＃x9]は＃x9を使用して表示され、[＃xA]は#xAを使用して表示され、[＃xD]は#xDを使用して表示され、[＃x20]は＃x20を使用して表示されることに注意してください。 Attribute = "[＃xA]
[＃x9]データ1 [#xD] [＃xA]
データ2 "
A. Attribute="data 1[#x20]data 2"
B. Attribute="[#x20][#x20]data 1[#x20]data 2"
C. Attribute="[#xA][#x9]data 1[#xD][#xA]data 2"W Courier Newlr Z
D. Attribute="[#xA]data 1[#xA]data 2"
Answer: B

NEW QUESTION: 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. Determine whether the solution meets the stated goals.
Your network contains an Active Directory domain named contoso.com. The domain contains a DNS server named Server1. All client computers run Windows 10.
On Server1, you have the following zone configuration.

You need to ensure that all of the client computers in the domain perform DNSSEC validation for the fabrikam.com namespace.
Solution: From a Group Policy object (GPO) in the domain, you add a rule to the Name Resolution Policy Table (NRPT).
Does this meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Explanation
The NRPT stores configurations and settings that are used to deploy DNS Security Extensions (DNSSEC), and also stores information related to DirectAccess, a remote access technology.
Note: The Name Resolution Policy Table (NRPT) is a new feature available in Windows Server 2008 R2. The NRPT is a table that contains rules you can configure to specify DNS settings or special behavior for names or namespaces. When performing DNS name resolution, the DNS Client service checks the NRPT before sending a DNS query. If a DNS query or response matches an entry in the NRPT, it is handled according to settings in the policy. Queries and responses that do not match an NRPT entry are processed normally.
References: https://technet.microsoft.com/en-us/library/ee649207(v=ws.10).aspx

NEW QUESTION: 3
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?
A. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and tram using the full dataset.
B. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
D. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode
Answer: A

NEW QUESTION: 4
You are developing an application that will parse a large amount of text.
You need to parse the text into separate lines and minimize memory use while processing data.
Which object type should you use?
A. StringBuilder
B. JsonSerializer
C. DataContractSerializer
D. StringReader
Answer: D
Explanation:
There are many ways to separate a string into lines. With StringReader, we read lines from a string individually in the order they appear. This type enables us to access string data through a stream-oriented interface.