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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A data pipeline ingests clickstream data from various sources into a raw Snowflake table CRAW CLICKS). A transformation job then processes this data and loads it into a more structured 'CLICK EVENTS table, performing filtering, cleaning, and data enrichment. The data engineering team notices significant performance bottlenecks during this transformation process, leading to data freshness issues.
The team wants to optimize this process, considering the following:

A) Implement a change data capture (CDC) mechanism on the source systems to only ingest changed data into 'RAW CLICKS, reducing the overall data volume and the amount of data processed by the transformation job.
B) Use a larger virtual warehouse for the transformation job and partition the 'RAW CLICKS table on the ingestion timestamp to improve data pruning and reduce the amount of data processed during the transformation.
C) Create a materialized view on top of 'RAW CLICKS' that pre-computes the necessary transformations and aggregations, allowing the 'CLICK EVENTS' table to be populated directly from the materialized view.
D) Optimize the transformation queries by identifying and rewriting inefficient SQL patterns, ensuring appropriate use of joins, filtering conditions, and data type conversions.
E) Replace the transformation job with a series of smaller, more specialized jobs, each running on a separate virtual warehouse optimized for the specific task, and orchestrate these jobs using a data pipeline tool.

2. You are responsible for monitoring a critical data pipeline that loads data from an external Kafka topic into a Snowflake table 'ORDERS' Data anomalies have been frequently observed, impacting downstream reporting. You want to implement a solution that proactivelyidentifies and alerts on data quality issues such as missing values, invalid formats, and unexpected data distributions. Which combination of Snowflake features and approaches would be MOST effective for achieving this objective with minimal performance overhead on the pipeline itself?

A) Using Snowflake's 'VALIDATE' table function after the data load to check for data corruption and then trigger alerts based on the validation results.
B) Creating a separate Snowflake pipeline that reads from the same Kafka topic, performs data quality checks in real-time using Snowpipe and streams the results to an alert system.
C) Employing Snowflake's built-in statistics and histogram features to analyze data distribution in the 'ORDERS' table and configure alerts based on deviations from historical patterns, combined with a Snowflake Native App for data quality reporting.
D) Leveraging Snowflake's Data Governance features along with Snowpark UDFs to define and enforce data quality rules at the time of ingestion using a Python- based library like Great Expectations, configured to trigger alerts through Snowflake Notifications.
E) Implementing custom SQL-based data quality checks within a scheduled Snowflake task that runs after the data load and writing results to an audit table for monitoring.

3. You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
B) Set the 'value.converter.schemas.enable' property to 'true'.
C) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
D) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
E) Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.

4. A data engineering team has implemented a continuous data pipeline that loads data into a Snowflake table named 'SALES DATA' They notice that the pipeline intermittently experiences performance degradation, particularly during peak business hours. The team wants to implement alerts to proactively identify and address these performance issues. Which of the following approaches would be MOST effective for monitoring the pipeline and triggering alerts based on specific performance metrics related to data loading?

A) Implement a data streaming service that monitors the 'SALES_DATX table in real-time. The streaming service should track the number of rows inserted per minute and trigger an alert if the insertion rate drops below a predefined threshold. No Snowflake object or Alert required.
B) Enable Snowflake's query acceleration service. This service automatically analyzes query performance and identifies opportunities for optimization, removing the need for manual monitoring and alerting. Use Snowflake's resource monitors to track credit usage.
C) Create a Snowflake Alert based on a metric in the Account Usage views (e.g., that identifies when load durations for the data warehouse associated with the data pipeline exceed a specified threshold. Configure a Notification Integration to route alerts to a designated channel.
D) Create a Snowflake Task that periodically queries the 'QUERY_HISTORY view, calculates the average load duration for 'SALES_DATX, and triggers an alert if the duration exceeds a predefined threshold. Use a Stored Procedure to handle the alert logic and send notifications.
E) create a custom Snowflake Alert that triggers when the function for the 'SALES_DATA' table indicates significant delay in data loading. Use a Snowflake Notification Integration to send alerts via email or Slack.

5. 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) 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.
B) Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
C) 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.
D) 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.
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.

Solutions: