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我々社は完全にレビューされるDEA-C02学習教材を提供しし、DEA-C02資格認定試験に合格して資格認定を得ることを目指しています。当社の最新のDEA-C02 MogiExamのレビューの助けで、あなたは本当の試験の能力と専門技術を向上させることができます。我々社はDEA-C02 pdf vceでもって、今まで多くの受験生は資格試験にパースしたのを手伝ってあげました。我々ウェブサイトは、資格試験試験問題集でも優れています。特に、少ない時間とお金をかけるに、より迅速にDEA-C02認定試験に合格しようとしている方にお勧めです。試験のガイドとしてDEA-C02 MogiExam pdfを選択するのは、ITキャリアで成功するための保証です。

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

1. You are building a data pipeline to ingest clickstream data into Snowflake. The raw data is landed in a stage and you are using a Stream on this stage to track new files. The data is then transformed and loaded into a target table 'CLICKSTREAM DATA. However, you notice that sometimes the same files are being processed multiple times, leading to duplicate records in 'CLICKSTREAM DATA. You are using the 'SYSTEM$STREAM HAS DATA' function to check if the stream has data before processing. What are the possible reasons this might be happening, and how can you prevent it? (Select all that apply)

A) The auto-ingest notification integration is configured incorrectly, causing duplicate notifications to be sent for the same files. This is particularly applicable when using cloud storage event triggers.
B) The transformation process is not idempotent. Even with the same input files, it produces different outputs each time it runs.
C) The stream offset is not being advanced correctly after processing the files. Ensure that the files are consumed completely and a DML operation is performed to acknowledge consumption.
D) The 'SYSTEM$STREAM HAS DATA' function is unreliable and should not be used for production data pipelines. Use 'COUNT( on the stream instead.
E) The COPY INTO command used to load the files into Snowflake has the 'ON ERROR = CONTINUE option set, allowing it to skip corrupted files, causing subsequent processing to pick them up again.

2. You are responsible for ensuring data consistency across multiple Snowflake tables involved in a financial reporting system. You've noticed discrepancies in aggregate calculations between a 'TRANSACTIONS" table and a summary table 'MONTHLY REPORTS'. The 'TRANSACTIONS' table is frequently updated via streams and tasks. Which combination of the following strategies would be MOST effective in identifying and resolving these inconsistencies in near real-time?

A) Utilize Snowflake's Time Travel feature to compare the ' TRANSACTIONS' table and 'MONTHLY _ REPORTS' table at a specific point in time and identify the changes that led to the discrepancies.
B) Create a Snowflake alert that triggers when the difference in the total 'SALE_AMOUNT between the 'TRANSACTIONS' table and 'MONTHLY REPORTS' exceeds a predefined threshold within a specified time window.
C) Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
D) Implement a Snowflake task that periodically recalculates the 'MONTHLY_REPORTS' table from the 'TRANSACTIONS table and compares the results with the existing data, logging any discrepancies. Use a smaller warehouse size to minimize cost.
E) Implement data validation checks within the data pipeline (streams and tasks) that update the 'TRANSACTIONS' table to reject transactions that violate predefined business rules.

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

4. You have a Snowflake table 'ORDERS with columns 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, and 'TOTAL AMOUNT. You notice that many queries filtering by 'ORDER DATE are slow, even after enabling query acceleration. You decide to implement a caching strategy to improve performance. Which of the following approaches will be most effective in leveraging Snowflake's caching capabilities and improving the performance of date-filtered queries, especially when the data volume for each date is large and varied? Assume virtual warehouse is medium size.

A) Create a clustered table on 'ORDER_DATE. This will physically organize the data on disk, allowing Snowflake to quickly retrieve the relevant data for date- filtered queries.
B) Create a materialized view that pre-aggregates the data by 'ORDER_DATE , such as calculating the sum of 'TOTAL_AMOUNT for each date. This will allow Snowflake to serve the results directly from the materialized view for queries that require aggregation.
C) Increase the data retention period for the 'ORDERS' table. A longer retention period will ensure that more data is available in the Snowflake cache.
D) Use after running a query filtered by 'ORDER_DATE'. This will cache the result of the query in the current session for subsequent queries with the same filter.
E) Apply a WHERE clause with a date range in all the SELECT statements. This forces the metadata caching.

5. You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
B) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
C) Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
D) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
E) Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.

質問と回答：