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NEW QUESTION: 1
Service Consumer A sends a request message to Service A (1) after which Service A retrieves financial data from Database A (2). Service A then sends a request message with the retrieved data to Service B (3). Service B exchanges messages with Service C (4) and Service D (5), which perform a series of calculations on the data and return the results to Service A.
Service A uses these results to update Database A (7) and finally sends a response message to Service Consumer A (8). Component B has direct, independent access to Database A and is fully trusted by Database A.
Both Component B and Database A reside within Organization A.
Service Consumer A and Services A, B, C, and D are external to the organizational boundary of Organization A.

Service A has recently experienced an increase in the number of requests from Service Consumer A.
However, the owner of Service Consumer A has denied that Service Consumer A actually sent these requests. Upon further investigation it was determined that several of these disclaimed requests resulted in a strange behavior in Database A, including the retrieval of confidential data. The database product used for Database A has no feature that enables authentication of consumers. Furthermore, the external service composition (Services A, B, C, D) must continue to operate at a high level of runtime performance.
How can this architecture be improved to avoid unauthenticated access to Database A while minimizing the performance impact on the external service composition?
A. The Brokered Authentication pattern is applied so that each service consumer generates a pair of private/public keys and sends the public key to Service A.
When any service in the external service composition (Services A, B, C, and D) sends a request message to another service, the request message is signed with the private key of the requesting service (the service acting as the service consumer). The service then authenticates the request using the already established public key of the service consumer. If authentication is successful, the service generates a symmetric session key and uses the public key of the service consumer to securely send the session key back to the service consumer. All further communication is protected by symmetric key encryption. Because all service consumers are authenticated, all external access to Database A is secured.
B. Service Consumer A generates a pair of private/public keys (Public Key E and Private Key D) and sends the public key to Service A.
Service A can use this key to send confidential messages to Service Consumer A because messages encrypted by the public key of Service Consumer A can only be decrypted by Service A The Data Origin Authentication pattern can be further applied so that Service A can authenticate Service Consumer A by verifying the digital signature on request messages. The Message Screening pattern is applied to a utility service that encapsulates Database A in order to prevent harmful input.
C. A utility service is established to encapsulate Database A and to carry out the authentication of all access to the database by Service A and any other service consumers.
To further support this functionality within the utility service, an identity store is introduced.
This identity store is also used by Service A which is upgraded with its own authentication logic to avoid access by malicious service consumers pretending to be legitimate service consumers. In order to avoid redundant authentication by services within the external service composition, Service A creates a signed SAML assertion that contains the service consumer's authentication and authorization information.
D. Implement a firewall between Service Consumer A and Service A.
All access to Service A is then controlled by the firewall rules. The firewall contains embedded logic that authenticates request messages and then forwards permitted messages to Service A.
Moreover, the firewall can implement the Message Screening pattern so that each incoming message is screened for malicious content. This solution minimizes the security processing performed by Service A in order to maintain the performance requirements of the external service composition.
Answer: C

NEW QUESTION: 2
Application data that comes from a C or COBOL application, or consists of fixed-format binary dat a should belong to which message domain?
A. DFDL domain
B. DataObject domain
C. SOAP domain
D. MIME domain
Answer: A

NEW QUESTION: 3
What is the maximum number of WDS links per radio that the Aruba mesh router supports?
A. 0
B. 1
C. 2
D. 3
Answer: A

NEW QUESTION: 4
A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month's sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
* You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
* You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
* You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?
A. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.
B. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
C. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
D. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the
Answer: B
Explanation:
Explanation
Specify the path.
Example:
The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.
from azureml.core import Workspace, Datastore, Dataset
datastore_name = 'your datastore name'
# get existing workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name
datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore
datastore_paths = [(datastore, 'weather/2018/11.csv'),
(datastore, 'weather/2018/12.csv'),
(datastore, 'weather/2019/*.csv')]
weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)