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DEA-C02 Latest Braindumps, DEA-C02 Best Vce

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q213-Q218):

NEW QUESTION # 213
You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A. Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
B. Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
C. Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
D. Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.
E. Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.

Answer: B,D,E

Explanation:
Snowflake encrypts data at rest by default (option A). Using network policies to restrict access based on IP address (option D) provides a layer of network security. Encrypting data client-side (option E) adds another layer of security beyond Snowflake's default encryption. Masking policies (option B) are applied to tables, not directly to the stage. While temporary stages exist, they are related to session lifecycle, not security inherently (option C).

NEW QUESTION # 214
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. 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.
C. 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.
D. Use Snowflake's row access policies to restrict access to the 'TRANSACTIONS' table, forcing users to only access the 'MONTHLY REPORTS table.
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.

Answer: A,C,E

Explanation:
Options B, C and D are the most useful. Option B leverages Time Travel to compare data at a specific point. Option C creates an alert to check the difference in total amount. Option D prevents inconsistent data from ever entering the table. Option A attempts to fix an issue, rather than preventing it and a small warehouse will take too long to complete. Option E restricts table access, but does not resolve inconsistent data, which is not what the prompt requests.

NEW QUESTION # 215
You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

A. Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
B. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.
C. Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
D. Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.
E. Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.

Answer: D

Explanation:
Python UDTFs with LATERAL FLATTEN are the most efficient and scalable for JSON parsing in Snowflake due to their ability to leverage Snowflake's vectorized execution engine. Using LATERAL FLATTEN inside the UDTF avoids unnecessary data movement and allows for optimized JSON processing. SQL UDFs, Java UDFs, JavaScript UDFs and SQL with JSON_VALUE are generally less performant for complex JSON parsing at scale compared to Python UDTFs leveraging the Snowflake engine.

NEW QUESTION # 216
A data engineering team uses Snowflake to analyze website clickstream data stored in AWS S3. The data is partitioned by year and month in the S3 bucket. They need to query the data frequently for reporting purposes but don't want to ingest the entire dataset into Snowflake due to storage costs and infrequent full dataset analysis. Which approach is the MOST efficient and cost-effective way to enable querying of this data in Snowflake?

A. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
B. Load all the data into a Snowflake table and create a materialized view on top of the table to pre-aggregate the data for reporting.
C. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
D. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
E. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.

Answer: C

Explanation:
Using an external table pointing to the S3 bucket is the most efficient and cost-effective approach. It allows you to query the data directly in S3 without ingesting it into Snowflake, saving on storage costs. Partitioning metadata further optimizes query performance by allowing Snowflake to only scan relevant partitions based on the query criteria.

NEW QUESTION # 217
You have a Snowflake table called 'ORDERS' with a column named 'ORDER DETAILS that contains JSON data'. You need to create a UDF to extract a specific value from the JSON and return it. The JSON structure is consistent: '{'customer': {'name': 'John Doe', 'email': 'john.doe@example.com'}, 'items': [{'item id': 1, 'quantity': 2}, {'item id': 2, 'quantity': You want to extract the customer's email address. Which of the following UDF definitions is the most efficient and correct?

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

Answer: C

Explanation:
Option A is the most efficient and correct. Since the input is a VARIANT, Snowflake automatically parses the JSON. Using order_details:customer:email::VARCHAR directly accesses the nested fields in a performant manner within SQL. Using 'PARSE_JSON' when the input is already a VARIANT (Option B) is unnecessary and inefficient. Option C uses Javascript that while valid is slower than pure SQL solution when processing JSON. Option D using GET_PATH is valid, but not native to the syntax which is slightly less efficient. Option E is deprecated function that is no longer used, thus is invalid.

NEW QUESTION # 218
......

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