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DEA-C02퍼펙트최신덤프공부 - DEA-C02시험유형

취직을 원하시나요? 승진을 원하시나요? 연봉인상을 원하시나요? 무엇을 원하시든 국제적으로 인정받은 IT인증자격증을 취득하는것이 길입니다. Snowflake인증 DEA-C02시험은 널리 인정받는 인기자격증의 시험과목입니다. Snowflake인증 DEA-C02시험을 패스하여 자격증을 취득하면 소원이 이루어집니다. Fast2test의Snowflake인증 DEA-C02덤프는 시험패스율이 높아Snowflake인증 DEA-C02시험준비에 딱 좋은 공부자료입니다. Fast2test에서 덤프를 마련하여 자격증취득에 도전하여 인생을 바꿔보세요.

지금 같은 경쟁력이 심각한 상황에서Snowflake DEA-C02시험자격증만 소지한다면 연봉상승 등 일상생활에서 많은 도움이 될 것입니다.Snowflake DEA-C02시험자격증 소지자들의 연봉은 당연히Snowflake DEA-C02시험자격증이 없는 분들보다 높습니다. 하지만 문제는Snowflake DEA-C02시험패스하기가 너무 힘듭니다. Fast2test는 여러분의 연봉상승을 도와 드리겠습니다.

>> DEA-C02퍼펙트 최신 덤프공부 <<

DEA-C02시험유형 - DEA-C02최고덤프

Fast2test 의 학습가이드에는Snowflake DEA-C02인증시험의 예상문제, 시험문제와 답입니다. 그리고 중요한 건 시험과 매우 유사한 시험문제와 답도 제공해드립니다. Fast2test 을 선택하면 Fast2test 는 여러분을 빠른시일내에 시험관련지식을 터득하게 할 것이고Snowflake DEA-C02인증시험도 고득점으로 패스하게 해드릴 것입니다.

최신 SnowPro Advanced DEA-C02 무료샘플문제 (Q175-Q180):

질문 # 175
You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A. Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.
B. Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
C. JavaScript UDF utilizing regular expressions for simple string replacements.
D. SQL UDF with nested CASE statements for address standardization.
E. Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.

정답：E

설명：
Python UDTFs with Anaconda packages offer the best balance of flexibility, performance, and ease of use for complex data transformations requiring external libraries. Snowflake's integration with Anaconda allows for the seamless use of popular data science and engineering libraries, making Python UDTFs ideal for tasks like address standardization. Java can be useful, but the overhead of JAR management and potentially less efficient integration with Snowflake's execution engine can be a disadvantage. SQL and JavaScript offer limited expressiveness for complex tasks requiring external libraries. While Scala is powerful, it can present a steeper learning curve and may not be as widely adopted as Python within the Snowflake ecosystem for UDTFs.

질문 # 176
You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A. Relying solely on Snowflake's Time Travel feature with the default data retention period.
B. Regularly creating full clones of the 'SALES_DATR table to a separate database.
C. Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
D. Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
E. Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.

정답：C

설명：
Option C is correct: Streams provide a granular way to capture DML changes. While Time Travel provides recovery, a stream specifically captures the changes, allowing for more controlled replay and point-in-time recovery. Full clones are resource-intensive, "UNDROP" is for dropped tables, not general recovery, and relying solely on default Time Travel may not meet specific recovery time objectives.

질문 # 177
A Snowflake data engineer is troubleshooting a performance issue with a query that retrieves data from a large table (TRANSACTIONS). The table has a VARIANT column containing semi-structured JSON data representing transaction details. The query uses several LATERAL FLATTEN functions to extract specific fields from the JSON and filters the data based on these extracted values. Despite having adequate virtual warehouse resources, the query is running slower than expected. Identify the MOST effective strategy to improve the performance of this query:

A. Create a search optimization service on the TRANSACTIONS table for the VARIANT column.
B. Create a new table with pre-extracted fields from the VARIANT column and use this table in the query instead of the LATERAL FLATTEN operations.
C. Rewrite the query to use regular expressions instead of LATERAL FLATTEN for extracting the fields from the JSON data.
D. Increase the virtual warehouse size to provide more memory for processing the JSON data.
E. Convert the VARIANT column to a VARCHAR column and store the JSON data as a string.

정답：B

설명：
Pre-extracting the required fields from the VARIANT column into separate columns in a new table significantly improves query performance by eliminating the need for expensive LATERAL FLATTEN operations at query time. Option A might help slightly, but pre-extraction is more impactful. Option C is unlikely to be faster. Option D, while applicable to VARIANT columns, is better suited for point lookups, not large-scale extraction and filtering. Option E would negate the benefits of the VARIANT data type.

질문 # 178
A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A. Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
B. Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
C. Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
D. Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.
E. Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.

정답：D

설명：
Option E is the most secure and scalable approach. It tokenizes the data during the load process, minimizing the amount of time the raw data resides in Snowflake. Using a UDF in a masking policy (options A and C) tokenizes the data on read, meaning the raw data is stored in Snowflake. Option B, using a stored procedure and , can be less efficient for large datasets. Data sharing raw data (Option D) defeats the purpose of tokenization for the source environment.

질문 # 179
You are tasked with building a data pipeline to process image metadata stored in JSON format from a series of URLs. The JSON structure contains fields such as 'image_url', 'resolution', 'camera_model', and 'location' (latitude and longitude). Your goal is to create a Snowflake table that stores this metadata along with a thumbnail of each image. Given the constraints that you want to avoid downloading and storing the images directly in Snowflake, and that Snowflake's native functions for image processing are limited, which of the following approaches would be most efficient and scalable?

A. Create a Python-based external function that fetches the JSON metadata and image from their respective URLs. The external function uses libraries like PIL (Pillow) to generate a thumbnail of the image and returns the metadata along with the thumbnail's Base64 encoded string within a JSON object.
B. Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$URL_GET, extracts the image URL from the metadata, downloads the image using 'SYSTEM$URL_GET , generates a thumbnail using SQL scalar functions, and stores the metadata and thumbnail in a Snowflake table.
C. Store just the 'image_url' in snowflake. Develop a separate application using any programming language to pre generate the thumbnails and host those at publicly accessible URLs. Within Snowflake, create a view to generate the links for image and thumbnail using 'CONCAT.
D. Create a Snowflake external table that points to an external stage which holds the JSON metadata files. Develop a spark process to fetch image URL, create thumbnails and store as base64 encoded strings in an external stage, create a view using the external table and generated thumbnails data
E. Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.

정답：A,C

설명：
Option C is the most appropriate solution. By using an external function with Python and libraries like PIL, you can efficiently handle image processing tasks that are difficult or impossible to perform natively within Snowflake. The external function encapsulates the image processing logic, keeping the Snowflake SQL code cleaner. Option E is also a valid solution as it leverages external processing. Option A is not performant as it tries to download image in snowflake which is not the best way to process image. Option B is not recommended because using JavaScript UDFs for binary data (images) can be inefficient. External Tables as described in Option D require pre-processing of data and storage to an external stage. Option D doesn't use the 'SYSTEM$URL GET' function that this question is trying to assess.

질문 # 180
......

Snowflake DEA-C02인증시험도 어려울 뿐만 아니라 신청 또한 어렵습니다.Snowflake DEA-C02시험은 IT업계에서도 권위가 있고 직위가 있으신 분들이 응시할 수 있는 시험이라고 알고 있습니다. 우리 Fast2test에서는Snowflake DEA-C02관련 학습가이드를 제동합니다. Fast2test 는 우리만의IT전문가들이 만들어낸Snowflake DEA-C02관련 최신, 최고의 자료와 학습가이드를 준비하고 있습니다. 여러분의 편리하게Snowflake DEA-C02응시하는데 많은 도움이 될 것입니다.

DEA-C02시험유형: https://kr.fast2test.com/DEA-C02-premium-file.html

안심하시고Fast2test 를 선택하게 하기 위하여, Fast2test에서는 이미Snowflake DEA-C02인증시험의 일부 문제와 답을 사이트에 올려놨으니 체험해보실 수 있습니다, Snowflake DEA-C02퍼펙트 최신 덤프공부 많은 분들이 PDF버전을 먼저 공부한후 소프트웨어버전이나 온라인버전으로 실력테스트를 진행하고 있는데 세가지 버전중 한가지 버전만 구매하셔도 되고 원하시는 두가지 버전을 구매하셔도 되고 패키지로 세가지 버전을 모두 구매하셔도 됩니다, Snowflake DEA-C02퍼펙트 최신 덤프공부 패스할확율은 아주 낮습니다, DEA-C02시험덤프자료는 최신 시험문제 커버율이 높아 시험패스가 아주 간단합니다.

준호는 아이 같은 단원들의 반응에 내심 웃음을 참기 어려웠다, 아DEA-C02시험유형이가 위험할 수도 있다는 말을 들은 뒤 몹시 불안해하던 연수는 함께 병원에 머물러준 설에게 이상할 정도로 의존적인 모습을 보였다, 안심하시고Fast2test 를 선택하게 하기 위하여, Fast2test에서는 이미Snowflake DEA-C02인증시험의 일부 문제와 답을 사이트에 올려놨으니 체험해보실 수 있습니다.

퍼펙트한 DEA-C02퍼펙트 최신 덤프공부 뎜프데모

많은 분들이 PDF버전을 먼저 공부한후 소프트웨어버전이나 온라인버전으로 실력테DEA-C02스트를 진행하고 있는데 세가지 버전중 한가지 버전만 구매하셔도 되고 원하시는 두가지 버전을 구매하셔도 되고 패키지로 세가지 버전을 모두 구매하셔도 됩니다.

패스할확율은 아주 낮습니다, DEA-C02시험덤프자료는 최신 시험문제 커버율이 높아 시험패스가 아주 간단합니다, Fast2test의Snowflake DEA-C02시험자료의 문제와 답이 실제시험의 문제와 답과 아주 비슷합니다.