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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 2
  • Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 3
  • Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 4
  • Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 5
  • Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.

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DEA-C01 - Updated SnowPro Advanced: Data Engineer Certification Exam Test Dates

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q206-Q211):

NEW QUESTION # 206
A company uses AWS Glue ETL pipelines to process data. The company uses Amazon Athena to analyze data in an Amazon S3 bucket.
To better understand shipping timelines, the company decides to collect and store shipping and delivery dates in addition to order data. The company adds a data quality check to ensure that shipping date is greater than order date and that delivery date is greater than shipping date.
Orders that fail the quality check must be stored in a second S3 bucket.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
AWS Glue Data Quality is the most cost-effective choice because it is built for ETL data validation and supports custom rules that compare multiple columns, such as ensuring shipping date is after order date and delivery date is after shipping date. It can also identify the specific records that fail validation so those failed orders can be written to a separate Amazon S3 bucket as part of the ETL flow.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/glue-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/tutorial-data-quality.html
https://docs.aws.amazon.com/glue/latest/dg/dqdl-rule-types-CustomSql.html


NEW QUESTION # 207
Mark the Incorrect Statements with respect to types of streams supported by Snowflake?

Answer: C

Explanation:
Explanation
Standard Stream:
Supported for streams on tables, directory tables, or views. A standard (i.e. delta) stream tracks all DML changes to the source object, including inserts, updates, and deletes (including table trun-cates). This stream type performs a join on inserted and deleted rows in the change set to provide the row level delta. As a net effect, for example, a row that is inserted and then deleted between two transactional points of time in a table is removed in the delta (i.e. is not returned when the stream is queried).
Append-only Stream:
Supported for streams on standard tables, directory tables, or views. An append-only stream tracks row inserts only. Update and delete operations (including table truncates) are not recorded. For ex-ample, if 10 rows are inserted into a table and then 5 of those rows are deleted before the offset for an append-only stream is advanced, the stream records 10 rows.
An append-only stream returns the appended rows only and therefore can be much more performant than a standard stream for extract, load, transform (ELT) and similar scenarios that depend exclu-sively on row inserts. For example, a source table can be truncated immediately after the rows in an append-only stream are consumed, and the record deletions do not contribute to the overhead the next time the stream is queried or consumed.
Insert-only Stream:
Supported for streams on external tables only. An insert-only stream tracks row inserts only; they do not record delete operations that remove rows from an inserted set (i.e. no-ops). For example, in-between any two offsets, if File1 is removed from the cloud storage location referenced by the ex-ternal table, and File2 is added, the stream returns records for the rows in File2 only. Unlike when tracking CDC data for standard tables, Snowflake cannot access the historical records for files in cloud storage.


NEW QUESTION # 208
A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises server are the same as the logs that are stored in the S3 bucket. Which solution will meet this requirement?

Answer: A

Explanation:
Computing a checksum during upload and storing it with the object provides an integrity value that can be compared later to confirm the uploaded object matches the original log file. Using an SDK-managed CRC32 checksum gives a straightforward, automated validation approach without additional processing infrastructure.


NEW QUESTION # 209
A media streaming company is moving its application to AWS. The company wants to avoid data quality issues in user engagement analytics. The company needs to be able to reprocess several days of historical data without duplicates and without interrupting current real-time processing.
Which solution will meet these requirements?

Answer: B

Explanation:
Extended retention in Amazon Kinesis Data Streams allows the company to keep several days of historical records available for replay instead of being limited to the default 24 hours. To avoid duplicates during both replay and live consumption, consumers should be idempotent because Kinesis processing can deliver duplicate records in retry scenarios. Using DynamoDB for checkpointing is the standard fit with Kinesis consumer processing, and using separate processing paths lets the company replay historical data without disrupting ongoing real-time processing.


NEW QUESTION # 210
Which stages support external tables?

Answer: D

Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.


NEW QUESTION # 211
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