社会と経済の発展につれて、多くの人は技術を勉強します。なぜならば、普通の職員にとって、SnowPro Advanced: Data Engineer (DEA-C02)資格証明書があるのは肝心な指標であると言えます。自分の能力を証明するために、SnowPro Advanced: Data Engineer (DEA-C02)試験に合格するのは不可欠なことです。弊社のSnowPro Advanced: Data Engineer (DEA-C02)真題を入手して、試験に合格する可能性が大きくなります。

周知のように、試験は嫌いことです。特に、SnowPro Advanced: Data Engineer (DEA-C02)日本語問題集のような試験は難しいです。候補者の難問を解決するために、我々はCertShikenのSnowPro Advanced: Data Engineer (DEA-C02)参考書を薦めます。もし弊社の問題集を信じられないなら、購入前にウエブサイトのデモをダウンロードして参考します。もちろん、購入前後には、何か質問があれば、ライブチャットとか、メールとか、いつでも弊社に連絡してください。

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SnowPro Advanced: Data Engineer (DEA-C02)試験参考書は認定試験に関する豊富な経験がある専門家が長年を通じて研究した試験参考書です。SnowPro Advanced: Data Engineer (DEA-C02)問題集の問題に対する回答についてですが、問題の回答が全部正しいものでございますよ。一部分の問題は解析が付きます。SnowPro Advanced: Data Engineer (DEA-C02)問題集を利用したほかの受験者が試験に合格しましたので、ご安心にご利用ください。万が一、SnowPro Advanced: Data Engineer (DEA-C02)試験にぱすしないと、不合格の証明書をめーるで弊社に送ります。弊社はチェックしてから、返金のことを行います。

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Snowflake DEA-C02 試験シラバストピック:

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

1. A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

A) Option C
B) Option D
C) Option A
D) Option E
E) Option B

2. A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A) Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
B) Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.
C) Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
D) Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
E) Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.

3. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
B) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
C) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
D) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
E) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.

4. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
B) Create separate tables for each "PRODUCT_ID' to improve query performance.
C) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.
D) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
E) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.

5. You are tasked with designing a data pipeline that ingests JSON data from an external stage (AWS S3). The JSON files contain records for various product types, each having a different set of attributes. Some product types might have attributes that are not present in other types. You want to create a single Snowflake table that can accommodate all product types without defining a rigid schema upfront and also be queryable efficiently. Which of the following approaches, combining external tables, schema evolution and querying, would be MOST effective? (Choose two)

A) Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.
B) Create a single external table with a VARIANT column to store the entire JSON record for each product. Use LATERAL FLATTEN to extract specific attributes during querying.
C) Create a separate external table for each product type, defining the schema for each table based on the attributes present in the corresponding JSON files.
D) Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.
E) Load all the data into a raw Snowflake internal table. Use dynamic SQL to infer distinct product types and create views on top of the raw table for each product type.

質問と回答：