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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table 'raw_data' with columns 'id', 'timestamp', and 'payload'. A stream is defined on this table. A data pipeline reads changes from the stream and applies transformations before loading the data into a target table. However, the pipeline needs to handle cases where updates to the same 'id' occur multiple times within a short period, and only the latest version of the 'payload' should be processed. How can you achieve this idempotent processing of stream data to ensure only the latest payload is applied to the target table, avoiding duplicates and inconsistencies, using Snowflake streams?

A) Create a materialized view on the stream, grouping by 'id' and selecting the maximum 'timestamp' and corresponding 'payload'. Then, consume the materialized view instead of the stream.
B) Configure the stream with a unique key constraint on the Sid' column to prevent multiple updates for the same Sid' from being captured.
C) Before loading data into target table, create a temporary table by grouping Sid' and selecting the maximum 'timestamp' and corresponding 'payload' from stream. Finally, load this data into target table.
D) Use a regular Snowflake task to periodically merge the stream data into the target table, overwriting any existing records with the same Sid'.
E) When processing data from the stream, use a MERGE statement with a staging table. Load all stream changes into the staging table, then merge from the staging table to the target table using 'timestamp' to identify the latest version.

2. A Data Engineer needs to implement dynamic data masking for a PII column named in a table 'CUSTOMERS. The masking policy should apply only to users with the role 'ANALYST. If the user is not an 'ANALYST, the full 'EMAIL' address should be displayed. Which of the following is the MOST efficient and secure way to achieve this using Snowflake's masking policies?

A) Option A
B) Option B
C) Option C
D) Option E
E) Option D

3. You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

A) Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
B) Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
C) Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.
D) Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
E) Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.

4. A data warehousing team is experiencing inconsistent query performance on a large fact table C SALES FACT) that is updated daily. Some queries involving complex joins and aggregations take significantly longer to execute than others, even when run with the same virtual warehouse size. You suspect that the query result cache is not being effectively utilized due to variations in query syntax and the dynamic nature of the data'. Which of the following strategies could you implement to maximize the effectiveness of the query result cache and improve query performance consistency? Assume virtual warehouse size is large and the data is skewed across days.

A) Implement a data masking policy on the 'SALES_FACT table. Data masking will reduce the size of the data that needs to be cached, improving cache utilization.
B) Optimize the 'SALES_FACT table by clustering it on the most frequently used filter columns and enabling automatic clustering. This will improve data locality and reduce the amount of data that needs to be scanned.
C) Implement query tagging to standardize query syntax. By applying consistent tags to queries, you can ensure that similar queries are recognized as identical and reuse cached results.
D) Create a separate virtual warehouse specifically for running these queries. This will isolate the cache and prevent it from being invalidated by other queries.
E) Use stored procedures with parameters to encapsulate the queries. This will ensure that the query syntax is consistent, regardless of the specific parameters used.

5. You are building a data pipeline that involves ingesting data from AWS S3 into Snowflake using Snowpipe. The data arrives in small files frequently, and you are experiencing performance issues with delayed data availability. You need to optimize the pipeline for near real-time ingestion. Which combination of strategies will MOST effectively address this scenario?

A) Manually refresh the Snowpipe using the 'ALTER PIPE ... REFRESH' command every few minutes to force ingestion of new data.
B) Increase the warehouse size used for the Snowpipe load process and adjust the 'MAX FILE SIZE' parameter on the pipe definition to match the size of the incoming files.
C) Configure S3 event notifications to trigger Snowpipe only when a sufficient number of files have arrived in the S3 bucket, using a serverless function (like AWS Lambda) to manage the file count threshold.
D) Implement a micro-batching process using a third-party tool (like Apache Spark) to aggregate the small files into larger batches before loading them into S3, then configure Snowpipe to ingest the larger files.
E) Enable auto-ingest on the Snowpipe and increase the frequency of S3 event notifications to Snowflake. Combine this with clustering the target table on a relevant column to optimize query performance after loading.

Solutions: