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DEA-C02최신시험예상문제모음100%합격보장가능한최신버전인증덤프

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>> DEA-C02최신 시험 예상문제모음 <<

DEA-C02최신 시험 예상문제모음 최신 덤프공부자료

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최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q231-Q236):

질문 # 231
A healthcare provider stores patient data in Snowflake, including 'PATIENT ID', 'NAME, 'MEDICAL HISTORY , and 'INSURANCE ID. They need to comply with HIPAA regulations. As a data engineer, you need to ensure that PHI (Protected Health Information) is masked appropriately based on user roles. Which of the following steps are NECESSARY to achieve this using Snowflake's data masking features and RBAC? (Select all that apply)

A. Create custom roles representing different user groups within the organization (e.g., 'DOCTOR, 'NURSE, 'ADMIN') and grant them the necessary privileges to access the data, including 'SELECT on the tables and views containing patient data.
B. Grant the 'OWNERSHIP privilege on the 'PATIENT table to the 'ACCOUNTADMIN' role, ensuring complete control and management of the data by the administrator.
C. Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.
D. Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
E. Apply the created masking policies to the corresponding columns in the patient data tables, ensuring that the masking policies are designed to reveal only the necessary information based on the user's role (e.g., doctors see full medical history, nurses see limited medical history, admins see de-identified data).

정답：A,D,E

설명：
Options A, B, and C are all necessary steps for implementing data masking and RBAC for PHI protection. Identifying PHI and creating masking policies is crucial. Defining roles and granting privileges aligns access with job functions. Applying the masking policies enforces role-based data visibility. Option D is not as important, as another admin role may be more suitable (SECURITYADMIN) than the ACCOUNTADMIN, and option E, MFA does enhance security but is not directly related to Data Masking with RBAC in Snowflake.

질문 # 232
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

A. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
B. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.
C. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
D. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
E. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.

정답：B

설명：
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention

질문 # 233
You are tasked with implementing a projection policy in Snowflake to restrict access to certain columns of the 'EMPLOYEE table based on the user's role. The table contains columns like 'EMPLOYEE 'NAME, 'SALARY', and 'DEPARTMENT. Users with the 'HR MANAGER role should have access to all columns, while other users should only be able to see 'EMPLOYEE ID, 'NAME, and DEPARTMENT. The initial attempt to create the projection policy results in an error. What could be the reasons?

A. The user attempting to create the projection policy does not have the 'OWNERSHIP' privilege on the 'EMPLOYEE table.
B. Projection policies can only be applied at the database level, not at the table level.
C. The projection policy definition might contain syntax errors or reference non-existent roles or columns.
D. Projection policies are not supported in Snowflake.
E. The EMPLOYEE table must have row-level security policies enabled before applying a projection policy.

정답：A,C

설명：
Options D and E are correct. To create a projection policy, the user needs the 'OWNERSHIP privilege on the table. A syntax error can cause an error. Option A is incorrect since projection policies are applied at the table or view level. Projection policies exist in Snowflake, and do not depend on Row Access Policies to be enabled so B and C are incorrect.

질문 # 234
You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)

A. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
B. The file format used for the external table is incompatible with the data files in Blob Storage.
C. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
D. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
E. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.

정답：C,E

설명：
The two primary reasons for delays in Snowflake detecting new files in an external table are: 1) Incorrect configuration of the cloud provider's notification service (Azure Event Grid in this case). Snowflake relies on these notifications to be informed about new file arrivals. If the integration isn't set up correctly, Snowflake won't know when to refresh the metadata. 2) The parameter must be set to ' TRUE' for automatic metadata refresh to occur. If it's set to FALSE , manual refreshes are required using 'ALTER EXTERNAL TABLE ... REFRESH". Options D and E, although possible issues, won't directly cause a delay in detecting new files, but rather cause issues accessing files after detection. Option C is irrelevant as Snowflake's caching mechanism does not directly impact external table metadata refresh.

질문 # 235
You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

A. Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'
B. Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
C. Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
D. Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
E. Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.

정답：D

설명：
Using Snowflake Anaconda environments allows you to manage Python dependencies for UDFs. Creating a Python UDF referencing the environment and using the 'SECURE keyword ensures both dependency management and code protection. Uploading libraries as internal stages and using Java UDFs is an unnecessarily complex approach. Snowflake does not automatically manage dependencies; they must be explicitly specified through Anaconda. Creating a Python inside a Javascript UDF is not a supported pattern

질문 # 236
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