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NEW QUESTION: 1
Click the Exhibit button.
user@host> show security flow session extensive
Session ID: 1173, Status: Normal
Flag: Ox0
Policy name: two/6
Source NAT pool: interface, Application: junos-ftp/1
Dynamic application: junos:UNKNOWN,
Application traffic control rule-set: INVALID, Rule: INVALID
Maximum timeout: 1800, Current timeout: 1756
Session State: Valid
Start time: 4859, Duration: 99
In: 172.20.103.10/56457 --> 10.210.14.130/21;tcp,
Interface: vlan.103,
Session token: Ox8, Flag: Ox21
Route: 0x100010, Gateway: 172.20.103.10, Tunnel: 0
Port sequence: 0, FIN sequence: 0, FIN state: 0,
Pkts: 12, Bytes: 549
Out: 10.210.14.130/21 --> 10.210.14.133/18698;tcp,
Interface: ge-0/0/0.0,
Session token: 0x7, Flag: Ox20
Route: Oxf0010, Gateway: 10.210 14.130, Tunnel: 0
Port sequence: 0, FIN sequence: 0,
FIN state: 0,
Pkts: 8, Bytes: 514
Total sessions: 1
A user complains that they are unable to download files using FTP. They are able to connect to the remote site, but cannot download any files. You investigate and execute the show security flow session extensive command to receive the result shown in the exhibit.
What is the cause of the problem?
A. Passive mode FTP is not enabled.
B. The FTP ALG has been disabled.
C. The NAT translation is incorrect.
D. The FTP session is using the wrong port number.
Answer: B

NEW QUESTION: 2
Welches der folgenden Ziele ist eines der Hauptziele der Servicestrategie?
A. Entwerfen und Erstellen von Prozessen, die den Geschäftsanforderungen entsprechen
B. Service Management in ein strategisches Asset verwandeln
C. Um die Bedeutung von Dienstleistungen für die Weltwirtschaft zu unterstreichen
D. Bereitstellung detaillierter Spezifikationen für das Design von IT-Services
Answer: D

NEW QUESTION: 3
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Create and start a Compute Instance
To train and deploy models using Azure Machine Learning designer, you need compute on which to run the training process, test the model, and host the model in a deployed service.
There are four kinds of compute resource you can create:
Compute Instances: Development workstations that data scientists can use to work with data and models.
Compute Clusters: Scalable clusters of virtual machines for on-demand processing of experiment code.
Inference Clusters: Deployment targets for predictive services that use your trained models.
Attached Compute: Links to existing Azure compute resources, such as Virtual Machines or Azure Databricks clusters.
Step 2: Create and run a training pipeline..
After you've used data transformations to prepare the data, you can use it to train a machine learning model. Create and run a training pipeline Step 3: Create and run a real-time inference pipeline After creating and running a pipeline to train the model, you need a second pipeline that performs the same data transformations for new data, and then uses the trained model to inference (in other words, predict) label values based on its features. This pipeline will form the basis for a predictive service that you can publish for applications to use.
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/