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Snowflake DEA-C02최신버전덤프공부자료 - DEA-C02시험패스자료

Snowflake인증 DEA-C02시험을 준비하기 위해 잠도 설쳐가면서 많이 힘들죠? ExamPassdump덤프가 고객님의 곁을 지켜드립니다. ExamPassdump에서 제공해드리는Snowflake인증 DEA-C02덤프는 실제Snowflake인증 DEA-C02시험문제를 연구하여 만든 공부자료이기에 최고의 품질을 자랑합니다. ExamPassdump덤프를 열심히 공부하여 멋진 IT전문가의 꿈을 이루세요.

ExamPassdump의 도움을 받겠다고 하면 우리는 무조건 최선을 다하여 한번에 패스하도록 도와드릴 것입니다. 또한 일년무료 업뎃서비스를 제공합니다. 중요한 건 덤프가 갱신이 되면 또 갱신버전도 여러분 메일로 보내드립니다. 망설이지 마십시오. 우리를 선택하는 동시에 여러분은DEA-C02시험고민을 하시지 않으셔도 됩니다.빨리 우리덤프를 장바구니에 넣으시죠.

>> Snowflake DEA-C02최신버전 덤프공부자료 <<

DEA-C02시험패스자료 - DEA-C02시험문제모음

요즘 같은 인재가 많아지는 사회에도 많은 업계에서는 아직도 관련인재가 부족하다고 합니다.it업계에서도 이러한 상황입니다.Snowflake DEA-C02시험은 it인증을 받을 수 있는 좋은 시험입니다. 그리고ExamPassdump는Snowflake DEA-C02덤프를 제공하는 사이트입니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q218-Q223):

질문 # 218
A data team is using Snowflake to analyze sensor data from thousands of IoT devices. The data is ingested into a table named 'SENSOR READINGS' which contains columns like 'DEVICE ID', 'TIMESTAMP', 'TEMPERATURE', 'PRESSURE', and 'LOCATION' (a GEOGRAPHY object). Analysts frequently run queries that calculate the average temperature and pressure for devices within a specific geographic area over a given time period. These queries are slow, especially when querying data from multiple months. Which of the following approaches, when combined, will BEST optimize the performance of these queries using the query acceleration service?

A. Enable search optimization on 'TEMPERATURE and 'PRESSURE columns and enable query acceleration.
B. Enable Automatic Clustering on 'DEVICE_ID' , then enable query acceleration on the virtual warehouse.
C. Partition the ' SENSOR_READINGS table by 'TIMESTAMP (e.g., daily partitions). Enable search optimization on the 'LOCATION' column and enable query acceleration.
D. Create a materialized view that pre-calculates the average temperature and pressure by device and location. Then enable query acceleration on the virtual warehouse.
E. Cluster the table by 'LOCATION' and 'TIMESTAMP , and enable search optimization on the 'LOCATION' column, and then enable query acceleration.

정답：E

설명：
Clustering by 'LOCATION' and 'TIMESTAMP' will group related data together, allowing Snowflake to quickly identify the relevant data for spatial queries. Enabling search optimization on the 'LOCATION' column allows queries filtering by geographic area to be accelerated. This combination provides the best performance because it addresses both the time-based and spatial aspects of the queries. Partitioning isn't directly supported by Snowflake but Clustering plays the equivalent role, and search optimization on Geography objects is critical. Materialized views can help, but might not be flexible enough for ad-hoc analysis. Automatic Clustering on 'DEVICE_ID won't help with spatial or time-based filtering. Search optimization on temperature and pressure will not help in spatial search.

질문 # 219
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 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.
C. 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.
D. Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
E. Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.

정답：C

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 # 220
You need to implement a data masking policy on the 'EMAIL' column of the 'EMPLOYEES' table. The requirement is to redact the entire email address with 'XXXXX' if the user's role is 'PUBLIC'. If the user's role is 'ANALYST', the domain part of the email should be visible, but the username should be redacted. For all other roles, the full email should be visible. Which of the following SQL statements CORRECTLY implements this masking policy?

A. Option C
B. Option E
C. Option B
D. Option A
E. Option D

정답：B

설명：
Option E correctly uses the 'REGEXP_REPLACE function with the regular expression to replace everything before the '@' symbol with 'XXXXX', thus redacting the username while preserving the domain. The other options have issues with the regex, potentially redacting the entire email in the 'ANALYST' case or not redacting the username properly. Option A's regex @ will replace everything till the last occurrence of @, which is not desired here. B and D have similar issues as A with the regex.

질문 # 221
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. Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
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. 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).
D. Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.
E. 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.

정답：A,C,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 # 222
You have a table named 'TRANSACTIONS which is frequently queried by 'TRANSACTION_DATE and 'CUSTOMER ID. You want to define a clustering strategy for this table. You are aware that defining multiple clustering keys is possible. Given the following considerations, which of the following clustering strategies would provide the BEST performance AND minimize reclustering costs, assuming both columns have similar cardinality and are equally used in WHERE clauses? (Assume cost optimization is the most critical factor if performance difference is minimal.)

A. Create two separate tables: one clustered by 'TRANSACTION DATE and another clustered by 'CUSTOMER ID', and use appropriate views to redirect queries to the correct table.
B.
C.
D.
E.

정답：B,C

설명：
Clustering by both and is beneficial when queries frequently filter on both columns. Either order, TRANSACTION_DATE followed by CUSTOMER_ID, or CUSTOMER_ID followed by TRANSACTION_DATE, could provide similar performance depending on query patterns. However, having both columns as clustering keys will allow better filtering of micro-partitions. Creating two separate tables as suggested in Option E introduces complexity in data maintenance (two copies of data) and query redirection logic, increasing overall cost. While it might provide optimal performance for specific query patterns, the cost is generally higher than using a composite clustering key when both keys are frequently used.

질문 # 223
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