弊社は無料でDEA-C02問題集のサンプルを提供します

受験者としてのあなたにDEA-C02認定試験に合格することができるために、我々のITの専門家たちが日も夜も努力して、最高のDEA-C02模擬問題集を開発します。数年以来の努力を通して、今まで、弊社は自分のDEA-C02試験問題集に自信を持って、弊社の商品で試験に一発合格できるということを信じています。

長時間の努力で開発されているDEA-C02模擬試験はMogiExamの受験者にヘルプを提供するという目標を叶うための存在ですから、的中率が高く、権威的で、内容が全面的です。我々のDEA-C02模擬問題集（SnowPro Advanced: Data Engineer (DEA-C02)）を利用すると、DEA-C02認定の準備をする時に時間をたくさん節約することができます。

信じられないなら、我々のサイトで無料なサンプルを利用してみることができます。お客様に弊社のDEA-C02模擬問題集の質量と3つのバーションの機能を了解するために、我々は3つのバーションのSnowflakeのDEA-C02のサンプルを無料で提供します。お客様は弊社のサイトでダウンロードすることができます。

弊社は行き届いたサービスを提供します

お客様に利便性を提供するために、弊社は全日24時間でお客様のSnowflakeのDEA-C02模擬問題集に関するお問い合わせを待っています。それに、弊社はお客様の皆様の要求に満たすために、DEA-C02問題集の三種類のバーションを提供します。お客様は自分の愛用するバーションを入手することができます。

それだけでなく、我々は最高のアフターサービスを提供します。その一、我々は一年間の無料更新サービスを提供します。すなわち、DEA-C02問題集をご購入になってからの一年で、我々MogiExamは無料の更新サービスを提供して、お客様の持っているDEA-C02 － SnowPro Advanced: Data Engineer (DEA-C02)模擬試験は最新のを保証します。この一年間、もしDEA-C02模擬問題集が更新されたら、弊社はあなたにメールをお送りいたします。

その二、お客様に安心で弊社のDEA-C02模擬試験を利用するために、我々は「試験に失敗したら、全額で返金します。」ということを承諾します。もしお客様はDEA-C02認定試験に合格しなかったら、我々はSnowflakeDEA-C02問題集の費用を全額であなたに戻り返します。だから、ご安心ください

Snowflake DEA-C02試験問題集をすぐにダウンロード：成功に支払ってから、我々のシステムは自動的にメールであなたの購入した商品をあなたのメールアドレスにお送りいたします。（12時間以内で届かないなら、我々を連絡してください。Note：ゴミ箱の検査を忘れないでください。）

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題:

1. You are designing a data pipeline to ingest streaming data from Kafka into Snowflake. The data contains nested JSON structures representing customer orders. You need to transform this data and load it into a flattened Snowflake table named 'ORDERS FLAT'. Given the complexities of real-time data processing and the need for custom logic to handle certain edge cases within the JSON payload, which approach provides the MOST efficient and maintainable solution for transforming and loading this streaming data into Snowflake?

A) Utilize a third-party ETL tool (like Apache Spark) to consume the data from Kafka, perform the JSON flattening and transformation logic, and then use the Snowflake connector to load the data into the 'ORDERS FLAT' table in batch mode.
B) Use Snowflake's built-in JSON parsing functions within a Snowpipe COPY INTO statement, combined with a 'CREATE VIEW' statement on top of the loaded data. The view will use 'LATERAL FLATTEN' to present the data in the desired flattened structure without physically transforming the underlying data.
C) Implement a custom external function (UDF) written in Java to parse and transform the JSON data before loading it into Snowflake. Configure Snowpipe to call this UDF during the data ingestion process. This UDF will flatten the JSON structure and return a tabular format directly insertable into 'ORDERS FLAT.
D) Use Snowflake's Snowpipe with a COPY INTO statement that utilizes the 'STRIP OUTER ARRAY option to handle the JSON array, combined with a series of SQL queries with 'LATERAL FLATTEN' functions to extract the nested data after loading into a VARIANT column.
E) Create a Python UDF that calls 'json.loads()' to parse the JSON within Snowflake and then use SQL commands with 'LATERAL FLATTEN' to navigate and extract the desired fields into a staging table. Afterward, use a separate SQL script to insert from staging to the final table 'ORDERS FLAT

2. You are planning to monetize a dataset on the Snowflake Marketplace. You want to provide potential customers with sample data to evaluate before they purchase a full subscription. Which of the following strategies are valid and recommended for offering a free sample of your data within the Snowflake Marketplace? (Select all that apply)

A) Upload a sample CSV file to a publicly accessible S3 bucket and provide the link in the Marketplace listing description. Consumers can download and load this data into their own Snowflake account for evaluation.
B) Create a separate share containing a subset (e.g., a smaller number of rows or columns) of the full dataset and offer this share as a free trial listing on the Marketplace.
C) Provide the consumer with the script to create a database link to your data, allowing them read-only access to a pre-defined sample table, and then revoke the access after a set period.
D) Offer a 'free trial' subscription on the primary listing that automatically expires after a set period (e.g., 7 days), allowing customers to access the full dataset during the trial period. You will need to write custom code to manage trial expiration and data access restrictions based on the trial status.
E) Create a view that filters the dataset based on a sampling algorithm (e.g., 'SAMPLE ROW' clause) and share the view through the Marketplace.

3. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
B) Increase the size of the Snowflake virtual warehouse to provide more processing power.
C) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
D) Create a materialized view on top of the external table to pre-aggregate the data.
E) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table

4.

A) Create a virtual column for 'item_id' and 'price' using JSON path expressions and create indexes on these virtual columns.
B) Create a new table with columns for 'item_id' and 'price' using the 'EVENT DATA column. Refreshed in a regular interval and used in Downstream querying.
C) Create a view that casts the 'EVENT DATA' column to VARCHAR before extracting attributes.
D) Use the ' GET_PATH' function repeatedly to extract 'item_id' and 'price' in the main query.
E) Create a search optimization service for the table 'USER_ACTIVITY to help filtering data in downstream

5. You are tasked with loading a large dataset (50TB) of JSON files into Snowflake. The JSON files are complex, deeply nested, and irregularly structured. You want to maximize loading performance while minimizing storage costs and ensuring data integrity. You have a dedicated Snowflake virtual warehouse (X-Large).
Which combination of approaches would be MOST effective?

A) Load the JSON data using the COPY INTO command with gzip compression. Create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use materialized views to improve query performance.
B) Load the JSON data using the COPY INTO command with no pre-processing. Create a VIEW on top of the raw VARIANT column to flatten the data for querying.
C) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.
D) Use Snowpipe with auto-ingest, create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use search optimization on those projected columns.
E) Pre-process the JSON data using a Python script with Pandas to flatten the structure and convert it into a relational format like CSV. Then, load the CSV files using the COPY INTO command with gzip compression.

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