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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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 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.
Topic 4	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q262-Q267):

NEW QUESTION # 262
What is a characteristic of the operations of streams in Snowflake?

A. When a stream is used to update a target table the offset is advanced to the current time.
B. Whenever a stream is queried, the offset is automatically advanced.
C. Querying a stream returns all change records and table rows from the current offset to the current time.
D. Each committed and uncommitted transaction on the source table automatically puts a change record in the stream.

Answer: C

Explanation:
Explanation
A stream is a Snowflake object that records the history of changes made to a table. A stream has an offset, which is a point in time that marks the beginning of the change records to be returned by the stream. Querying a stream returns all change records and table rows from the current offset to the current time. The offset is not automatically advanced by querying the stream, but it can be manually advanced by using the ALTER STREAM command. When a stream is used to update a target table, the offset is advanced to the current time only if the ON UPDATE clause is specified in the stream definition. Each committed transaction on the source table automatically puts a change record in the stream, but uncommitted transactions do not.

NEW QUESTION # 263
A data engineer needs to validate the quality of files that are uploaded to an Amazon S3 bucket every day. The files are in CSV and JSON formats and schema variations exist. The data engineer needs a repeatable process to monitor data quality metrics such as null values, format inconsistencies, and outliers. The process must provide reusable rule-based profiling and scale across multiple datasets with minimal manual effort or custom code.
Which solution will meet these requirements?

A. Use AWS Glue DataBrew to configure profiling jobs and reusable recipe actions. Schedule the profiling jobs and reusable recipe actions to run against each dataset in Amazon S3.
B. Use AWS Glue extract, transform, and load (ETL) jobs with dynamic frames and the ResolveChoice class to align schemas and detect structural differences between files.
C. Use AWS Glue Studio to create a daily extract, transform, and load (ETL) pipeline that merges datasets and applies validation logic by using PySpark transformations.
D. Use AWS Glue DataBrew to generate profiling dashboards for each dataset. Monitor quality metrics through manual review.

Answer: A

Explanation:
AWS Glue DataBrew is designed for low-code data profiling and data quality validation across datasets stored in Amazon S3. It can generate profile statistics such as null values and outliers, and it supports reusable data quality rules and repeatable jobs, which matches the requirement for a scalable, low-effort process across CSV and JSON datasets with schema variation. Manual dashboard review alone would not provide the repeatable rule-based validation the question requires.
Reference:
https://docs.aws.amazon.com/databrew/latest/dg/profile.data-quality-rules.html
https://docs.aws.amazon.com/databrew/latest/dg/getting-started.02.html

NEW QUESTION # 264
A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.
B. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.
C. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks.
Load the processed data and the quality check results into a new S3 bucket.
D. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

Answer: A

Explanation:
Using a single AWS Glue ETL job to both transform the data and invoke Glue Data Quality checks lets you declaratively enforce recommended rules without spinning up separate tools. You can then write the cleansed data and, if desired, the quality metrics, directly into your Amazon RDS for MySQL instance. This serverless, end-to-end approach minimizes service sprawl and only incurs Glue and RDS costs, making it the most cost-effective with the least operational overhead.

NEW QUESTION # 265
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?

A. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
B. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 11,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
C. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK'));
D. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,query_id IS NOT NULL
5.task_name=>'MYTASK'));

Answer: C

Explanation:
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.

NEW QUESTION # 266
A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data.
Which solution will meet these requirements?

A. Use AWS Glue DataBrew to create the partitions for the AWS Glue table.
B. Set partition projection properties for the AWS Glue table.
C. Configure an AWS Glue crawler to run on a set schedule.
D. Use an AWS Lambda function to create the partitions for the AWS Glue table.

Answer: B

Explanation:
Partition projection in AWS Glue lets Athena automatically calculate partitions based on table properties without explicitly creating them each day. This eliminates the need for crawlers or Lambda jobs, reduces operational overhead, and adapts to schema changes while supporting year, month, and day partitions efficiently.

NEW QUESTION # 267
......

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