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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q269-Q274):

NEW QUESTION # 269
A data engineering team is building a data pipeline in Snowflake. They are using tasks and streams to incrementally load data into a fact table. The team needs to monitor the pipeline's performance and ensure data lineage. What are the valid and most effective techniques to ensure that this pipeline adheres to compliance and governance rules?

A. Implement Snowflake's Data Lineage and Object Dependencies features to track data flow automatically, create Alerts based on 'TASK HISTORY to monitor task failures, and enforce data masking and row-level security policies at the table level. Use Snowflake's tags to categorise and classify objects.
B. Use Account Usage views like 'TASK HISTORY and 'STREAM_LAG' to track task execution and stream latency, create stored procedures to log metadata about each pipeline run to a separate metadata table, and rely on developers to manually document the pipeline's data flow and policy enforcement.
C. Use a third-party data catalog to track lineage, monitor task performance via 'TASK_HISTORY, and ignore data masking and row-level security policies for simplicity in the initial implementation.
D. Leverage Snowflake's replication features for disaster recovery, monitor only the replication lag, and disable all security policies to improve performance since those tasks have already been validated during the initial deployment of the software.
E. Enable Snowflake Horizon features, which include Data Lineage, Object Dependencies and Discovery and integrate it with the data lake and also tag the data pipeline.

Answer: A,E

Explanation:
Option B and E offers the most comprehensive approach. Snowflake's Data Lineage and Object Dependencies features, combined with Alerts based on 'TASK HISTORY, offer automated monitoring and data flow tracking. Enforcing data masking and row-level security is crucial for data governance and compliance, and tagging enables easy categorisation and discovery. Though Horizon and Data Lineage offers data flow tracking, alerting for task failures, data masking and row-level security policies and tagging can be integrated together. Options A lacks automated lineage tracking and relies on manual documentation, which is error-prone. Option C ignores crucial security policies, which is unacceptable. Option D focuses only on disaster recovery and neglects security and monitoring aspects.

NEW QUESTION # 270
You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A. Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
B. Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
C. Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
D. Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.
E. Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.

Answer: A

Explanation:
Using Streams and Tasks for incremental updates (option B) is the most efficient and low-latency solution. It minimizes data processing time and cost compared to full refreshes (options A and C). Manual uploads (option D) are not automated. Sharing raw data files (option E) puts the burden of data processing on the consumer, which is less desirable for a data product.

NEW QUESTION # 271
You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A. Increase the number of Snowflake virtual warehouses to handle the increased load.
B. Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
C. Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
D. Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.
E. Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.

Answer: B,C

Explanation:
B and C are correct. Implementing client-side retry logic with exponential backoff (B) prevents overwhelming the service during transient errors. Using a message queue like Kafka (C) buffers the data, smoothing out traffic spikes and providing better resilience. A is less effective as scaling warehouses won't directly address client-side issues like retry logic and buffering. D can help but is not as effective as a buffering mechanism or robust retry strategy. E is incorrect as Snowpipe Streaming is designed for lower latency than classic Snowpipe.

NEW QUESTION # 272
You are developing a data pipeline that extracts data from an on-premise PostgreSQL database, transforms it, and loads it into Snowflake. You want to use the Snowflake Python connector in conjunction with a secure method for accessing the PostgreSQL database. Which of the following approaches provides the MOST secure and manageable way to handle the PostgreSQL connection credentials in your Python script when deploying to a production environment?

A. Hardcode the PostgreSQL username and password directly into the Python script.
B. Prompt the user for the PostgreSQL username and password each time the script is executed.
C. Store the PostgreSQL username and password in a configuration file (e.g., JSON or YAML) and load the file in the Python script.
D. Store the PostgreSQL username and password in environment variables and retrieve them in the Python script using 'os.environ'
E. Store the PostgreSQL username and password in a dedicated secrets management service (e.g., AWS Secrets Manager, HashiCorp Vault, Azure Key Vault) and retrieve them in the Python script using the appropriate API.

Answer: E

Explanation:
Option D, using a dedicated secrets management service, provides the most secure and manageable approach. Secrets management services are designed to securely store and manage sensitive information like database credentials. They offer features like encryption, access control, auditing, and versioning, making them the best choice for production environments. Option A is highly insecure. Options B and C are better than A but still less secure than using a secrets management service, as environment variables and configuration files can be accidentally exposed or committed to version control. Option E is impractical and insecure for automated pipelines.

NEW QUESTION # 273
You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

A. Increase the warehouse size to the largest available option (e.g., X-Large or larger).
B. Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.
C. Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
D. Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
E. Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.

Answer: B,D,E

Explanation:
Options B, C, and E address key aspects of performance optimization: B: Optimizing joins is crucial for large datasets. Using broadcast joins where applicable (smaller table fits in memory) and ensuring compatible data types between join keys can significantly reduce data shuffling and improve join performance. C: Caching (persisting) the DataFrame from the external stage using avoids re-reading the data from S3 for each operation, especially if the data is accessed multiple times (e.g., in multiple joins). E: The configuration of the external stage is critical. Using columnar formats like Parquet enables efficient data scanning and filtering. Partitioning the data in S3 based on the join keys allows Snowflake to prune unnecessary data during the read, reducing the amount of data processed. Increasing the warehouse size (Option A) might help, but it's often more cost-effective to optimize the data pipeline first. Reducing the number of partitions to 1 (Option D) would likely hurt performance, as it eliminates parallelism.

NEW QUESTION # 274
......

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