PASS GUARANTEED SNOWFLAKE - DEA-C02 - THE BEST SNOWPRO ADVANCED: DATA ENGINEER (DEA-C02) VCE FREE

Pass Guaranteed Snowflake - DEA-C02 - The Best SnowPro Advanced: Data Engineer (DEA-C02) Vce Free

Pass Guaranteed Snowflake - DEA-C02 - The Best SnowPro Advanced: Data Engineer (DEA-C02) Vce Free

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q46-Q51):

NEW QUESTION # 46
Consider the following Snowflake Javascript UDF designed to convert temperatures between Celsius and Fahrenheit:

Which statement regarding the UDF's security and behavior is MOST accurate?

  • A. The 'SECURE' keyword ensures that the UDF's code is completely hidden from all users, including those with the ACCOUNTADMIN role.
  • B. While the ' SECURE keyword hides the UDF's source code from users without sufficient privileges, users with the ACCOUNTADMIN role can still view the source code.
  • C. The UDF is vulnerable to SQL injection because the 'unit' parameter is not validated against a predefined list of acceptable values.
  • D. The UDF is not vulnerable to SQL injection because Javascript UDFs in Snowflake are sandboxed and prevent direct SQL execution.
  • E. The UDF will always return a value, even if the input 'unit is invalid, due to the implicit type conversion in JavaScript.

Answer: D

Explanation:
JavaScript UDFs in Snowflake are sandboxed, preventing SQL injection vulnerabilities. The SECURE keyword hides the UDF definition from users without appropriate privileges, but not from users with sufficient privileges like ACCOUNTADMIN. The UDF explicitly returns NULL for invalid unit values.


NEW QUESTION # 47
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. 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.
  • E. Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.

Answer: A,B

Explanation:
Options B and D provide the most effective solution for handling diverse JSON data with schema evolution and efficient querying. Option B allows for storing the entire JSON record in a VARIANT column, enabling flexibility in accommodating varying product attributes. LATERAL FLATTEN allows extracting specific attributes needed during querying. Option D further enhances this by using the VALIDATE function (part of COPY INTO when loading into a table using COPY INTO FROM @stage), even though in this case we are using an external table, to identify schema inconsistencies and handle them appropriately. Option A is not scalable, Option C requires a lot of code and is difficult to maintain and option E, requires too much SQL to execute, making it expensive.


NEW QUESTION # 48
You are the provider of a data product on the Snowflake Marketplace. You need to grant a trial access to a potential consumer You want to provide limited access for 7 days to specific tables in your database. Which of the following steps are REQUIRED to accomplish this?
(Select all that apply)

  • A. Contact Snowflake support to enable trial access for the consumer's account.
  • B. Monitor the consumer's query history to ensure they are only accessing the allowed tables.
  • C. Create a new role, grant USAGE privilege on the database and SELECT privilege on the specific tables to this role, and then grant this role to the trial consumer.
  • D. Grant OWNERSHIP on the specific tables to the consumer's account temporarily.
  • E. Create a new share specifically for the trial consumer, granting USAGE privilege on the database and SELECT privilege on the specific tables.

Answer: E

Explanation:
Option A is correct. You need to create a specific share granting the required privileges (USAGE on the database and SELECT on the tables) to the consumer's account. Option B suggests creating a role and granting it to the consumer; while possible, it's not the direct method using shares for Marketplace access. Option C is a monitoring task, not a setup step. Option D is incorrect; this can be done directly in Snowflake. Option E is incorrect; OWNERSHIP should not be granted temporarily as it gives full control over the object.


NEW QUESTION # 49
You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?

  • A. Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.
  • B. Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.
  • C. Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.
  • D. Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.
  • E. Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.

Answer: D

Explanation:
The best approach is to use a Snowpark Python UDF (option C) to pre-process and cleanse the JSON files. This allows for handling schema variations and correcting invalid JSON structures before loading them into Snowflake. 'ON ERROR = SKIP FILE might skip important data without proper investigation. A landing stage (option B) adds complexity and requires additional automation. Making the file format too permissive (option D) may lead to incorrect data loading. Disabling auto-refresh (option E) defeats the purpose of a continuous data pipeline.


NEW QUESTION # 50
You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

  • A. Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.
  • B. Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.
  • C. Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
  • D. Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
  • E. Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.

Answer: A,C,E

Explanation:
The correct answers are B, C, and D. Option B avoids redundant API calls by caching results. Option C handles API rate limits to prevent excessive charges. Option D reduces the number of API calls by pre-filtering irrelevant reviews. Option A will increase latency and is not a cost optimization. Option E is not always feasible if Snowflake's built-in functions are insufficient.


NEW QUESTION # 51
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