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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q290-Q295):

NEW QUESTION # 290
You are using Snowpipe to continuously load JSON data from an Azure Blob Storage container into a Snowflake table. The data contains nested JSON structures. You observe that some records are not being loaded into the table, and the 'VALIDATION MODE shows 'PARSE ERROR' for these records. Examine the following COPY INTO statement and the relevant error message from 'VALIDATION MODE', and identify the most likely cause of the problem. COPY INTO my_table FROM FILE FORMAT = (TYPE = JSON STRIP OUTER ARRAY = TRUE) ON ERROR = CONTINUE; Error Message (from VALIDATION MODE): 'JSON document is not well formed: invalid character at position 12345'

A. The file format definition is missing a 'NULL IF' parameter which is causing Snowflake to attempt to load string values that should be NULL.
B. The 'STRIP OUTER ARRAY' parameter is causing the issue because the incoming JSON data is not wrapped in an array. Remove the 'STRIP OUTER ARRAY' parameter from the COPY INTO statement.
C. The Snowflake table schema does not match the structure of the JSON data. Verify that the column names and data types in the table are compatible with the JSON fields.
D. The JSON data contains invalid characters or formatting errors at position 12345, as indicated in the error message. Cleanse the source data to ensure it is well-formed JSON before loading.
E. Snowpipe is encountering rate limiting issues with Azure Blob Storage. Implement retry logic in your Snowpipe configuration.

Answer: D

Explanation:
The error message 'JSON document is not well formed: invalid character at position 12345' clearly indicates that the source data contains invalid JSON. While schema mismatch (C) and rate limiting (D) can cause data loading issues, the specific error message points directly to corrupted JSON data (B). Option A is incorrect because the STRIP OUTER ARRAY parameter is only relevant if the file contains an array and might not be the cause of JSON parsing errors. E is possible, but the error points to invalid JSON and not simply invalid data for a particular column.

NEW QUESTION # 291
You are tasked with building a data pipeline that incrementally loads data from an external cloud storage location (AWS S3) into a Snowflake table named 'SALES DATA'. You want to optimize the pipeline for cost and performance. Which combination of Snowflake features and configurations would be MOST efficient and cost-effective for this scenario, assuming the data volume is substantial and constantly growing?

A. Develop a custom Python script that uses the Snowflake Connector for Python to connect to Snowflake and execute a COPY INTO command. Schedule the script to run on an EC2 instance using cron.
B. Employ a third-party ETL tool to extract data from S3, transform it, and load it into Snowflake using JDBC. Schedule the ETL process using the tool's built-in scheduler.
C. Create an external stage pointing to the S3 bucket. Create a Snowpipe with auto-ingest enabled, using an AWS SNS topic and SQS queue for event notifications. Configure the pipe with an error notification integration to monitor ingestion failures.
D. Use a Snowflake Task to regularly truncate and reload 'SALES DATA" from S3 using COPY INTO. This ensures data consistency.
E. Use a Snowflake Task scheduled every 5 minutes to execute a COPY INTO command from S3, with no file format specified, assuming the data is CSV and auto-detection will work.

Answer: C

Explanation:
Snowpipe with auto-ingest is the most efficient and cost-effective solution for continuously loading data into Snowflake from cloud storage. It leverages event notifications to trigger data loading as soon as new files are available, minimizing latency and compute costs. Option A lacks error handling and proper file format specification. Option C involves custom coding and infrastructure management. Option D introduces overhead and costs associated with a third-party ETL tool. Option E is inefficient as it truncates and reloads the entire table, losing any incremental loading benefits.

NEW QUESTION # 292
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. Create a Snowpipe pointing to the S3 bucket and ingest the data continuously into a Snowflake table.
B. Create a Snowflake external stage pointing to the S3 bucket, define an external table on the stage, and use partitioning metadata to optimize queries.
C. Use Snowflake's COPY INTO command to ingest data directly from S3 into a Snowflake table on a scheduled basis.
D. Create a Snowflake internal stage, copy the necessary files into the stage, and then load the data into a Snowflake table.
E. 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.

Answer: B

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 # 293
You're designing a Snowpark data transformation pipeline that requires running a Python function on each row of a large DataFrame. The Python function is computationally intensive and needs access to external libraries. Which of the following approaches will provide the BEST combination of performance, scalability, and resource utilization within the Snowpark architecture?

A. Create a Snowpark UDF using input_types=[StringType()], return_type=StringType())' and apply it to the DataFrame using
B. Create a Snowpark UDTF using gudtf(output_schema=StructType([StructField('result', StringType())]), and apply it to the DataFrame using with a lateral flatten operation.
C. Use 'DataFrame.foreach(lambda row: my_python_function(row))' to iterate through each row and apply the Python function.
D. Load the DataFrame into a Pandas DataFrame using and then apply the Python function using Pandas DataFrame operations.
E. Define a stored procedure in Snowflake and use it to execute the Python code on each row by calling it in a loop.

Answer: A,B

Explanation:
Options B and D are the best choices. UDFs and UDTFs allow you to leverage Snowflake's compute resources for parallel processing. The function execution happens on Snowflake's servers, close to the data, minimizing data transfer. By specifying 'packages=['my_package']' , you ensure that the external libraries are available in the execution environment. A UDF is suitable for one-to-one row transformations, while a UDTF is more appropriate if the Python function needs to return multiple rows for each input row (one-to-many). Option A, DataFrame.foreacW , is inefficient for large DataFrames as it processes rows sequentially. Option C, loading into Pandas, is also not ideal as it can lead to out-of-memory errors for very large DataFrames and transfers the data to the client machine. Option E, stored procedures with loops, is less scalable and efficient than UDFs or UDTFs.

NEW QUESTION # 294
You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

A. Partition the table by 'TRANSACTION DATE
B. Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
C. Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.
D. Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.
E. Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.

Answer: C

Explanation:
Clustering the table using a composite key of '(TRANSACTION_DATE, PRODUCT_CATEGORY)' is the most effective approach. This will physically organize the data so that micro-partitions contain data with similar values for both columns, significantly improving performance for queries filtering on both columns. Snowflake does not support indexes (A). Materialized views (B, D) can be helpful, but clustering the base table provides a more fundamental optimization. Partitioning (E) isn't possible in Snowflake. Clustering directly on composite columns addresses the physical data layout best for common query patterns.

NEW QUESTION # 295
......

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