DEA-C02 RELIABLE BRAINDUMPS SHEET, EXAM DEA-C02 PRICE

DEA-C02 Reliable Braindumps Sheet, Exam DEA-C02 Price

DEA-C02 Reliable Braindumps Sheet, Exam DEA-C02 Price

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q121-Q126):

NEW QUESTION # 121
You are tasked with loading Parquet files into Snowflake from an AWS S3 bucket. The Parquet files are compressed using Snappy compression and contain a complex nested schem a. Some of the columns contain timestamps with nanosecond precision. You want to create a Snowflake table that preserves the timestamp precision. Which COPY INTO statement options and table definition are MOST appropriate?

  • A. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
  • B. Table Definition: CREATE TABLE my_table (ts VARCHAR, other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' = PARSE TIMESTAMP(ts));
  • C. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE' VALIDATION_MODE = RETURN_ERRORS;
  • D. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE = PARQUET COMPRESSION = SNAPPY) ON_ERROR = 'SKIP_FILE';
  • E. Table Definition: CREATE TABLE my_table (ts TIMESTAMP NTZ(9), other_col VARCHAR); COPY INTO my_table FROM FILE FORMAT = (TYPE - - pARQUET COMPRESSION = AUTO) ON_ERROR = 'SKIP_FILE';

Answer: E

Explanation:
The correct approach is to define the timestamp column with TIMESTAMP NTZ(9) to preserve nanosecond precision. Also, setting COMPRESSION = AUTO is a good practice to let Snowflake automatically detect and handle the compression type, even though Snappy is explicitly mentioned. Option A is close, but AUTO compression is preferred for robustness. B would lose precision as timestamp_ntz defaults to (0), C converts TIMESTAMP to VARCHAR which causes issues with ordering. E will throw errors but does not solve the problem.


NEW QUESTION # 122
You are using the Snowflake REST API to insert data into a table named 'RAW JSON DATA. The JSON data is complex and nested, and you want to efficiently parse and flatten it into a relational structure. You have the following JSON sample:

Which SQL statement, executed after loading the raw JSON using the REST API, is the MOST efficient way to flatten the JSON and extract relevant fields into a new table named 'PURCHASES' with columns like 'EVENT TYPE', 'USER D', 'EMAIL', 'STREET, 'CITY', 'ITEM ID', and 'PRICE'?

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

Answer: B

Explanation:
Option C is the most efficient and correct. It uses the correct Snowflake syntax for accessing JSON elements with and for data type casting and also correctly uses the TABLE(FLATTEN()) function to handle the nested 'items' array. Option A utilizes a deprecated syntax LATERAL FLATTEN'. Options B, D and E don't handle the nested JSON structure properly or the flattening of the 'items' array, resulting in incomplete or incorrect data extraction.


NEW QUESTION # 123
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 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.
  • B. 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.
  • C. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.
  • D. 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.
  • E. 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.

Answer: A

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 # 124
You are tasked with building an ETL pipeline that ingests JSON logs from an external system via the Snowflake REST API. The external system authenticates using OAuth 2.0 client credentials flow. The logs are voluminous, and you want to optimize for cost and performance. Which of the following approaches are MOST suitable for securely and efficiently ingesting the data?

  • A. Use the Snowflake REST API directly from your ETL tool, handling OAuth token management in the ETL tool. Load data into a staging table, then use COPY INTO with a transformation to the final table.
  • B. Create a Snowflake external function that handles the API call and OAuth authentication. Use a stream on the external stage pointing to the external system's storage to trigger data loading into the final table.
  • C. Use Snowflake's Snowpipe with REST API by configuring the external system to directly push the logs to an external stage and configure Snowpipe to automatically ingest it.
  • D. Configure the ETL tool to write directly to Snowflake tables using JDBC/ODBC connection strings. Avoid the REST API due to its complexity.
  • E. Implement a custom API gateway using a serverless function (e.g., AWS Lambda, Azure Function) to handle authentication and batch the JSON logs before sending them to the Snowflake REST API. Write the API output to a Snowflake stage, then use COPY INTO to load into a final table.

Answer: A,E

Explanation:
Options A and C are the most suitable. Option A involves direct integration and option C introduces batching and serverless function to improve performance and manage authentication. Option B is incorrect because external functions cannot directly trigger data loading based on external stage events. Option D bypasses the REST API requirement and does not address authentication. Option E avoids the REST API entirely, which is against the problem requirement.


NEW QUESTION # 125
You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

  • A. Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
  • B. Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
  • C. Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
  • D. Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
  • E. Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE

Answer: D

Explanation:
Option B is the most comprehensive solution for secure data loading and PII protection. 'TRANSFORM' and JavaScript UDF masking during load prevent PII from being stored unmasked, enhancing security. Implementing masking policies provides granular control over access to the sensitive data post-load. It's more secure to avoid storing sensitive information even temporarily. Option A encrypts the data in transit but doesn't address masking. Option C requires dynamic data masking, but masking during copy is optimal. The correct way to copy and mask PII data is using a JavaScript UDF to mask the PII data during the load process, and then implement Masking Policies on the table.


NEW QUESTION # 126
......

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