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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q365-Q370):

NEW QUESTION # 365
A data engineer created a table named cloudtrail_logs in Amazon Athena to query AWS CloudTrail logs and prepare data for audits. The data engineer needs to write a query to display errors with error codes that have occurred since the beginning of 2024. The query must return the
10 most recent errors.
Which query will meet these requirements?

A. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessage order by TotalEvents desc limit 10;
B. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logs where errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessagelimit 10;
C. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere errorcode is not nulland eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by TotalEvents desclimit 10;
D. select count (*) as TotalEvents, eventname, errorcode, errormessage from cloudtrail_logswhere eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by eventname asc limit 10;

Answer: C

Explanation:

This query meets the requirements by:
1. Filtering results where errorcode is not null, so only error events are included.
2. Filtering by eventtime to include events occurring since the beginning of 2024.
3. Grouping by eventname, errorcode, and errormessage to summarize the error events.
4. Sorting by TotalEvents in descending order to show the most recent errors.
5. Limiting the results to the 10 most recent errors.

NEW QUESTION # 366
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A. Manually review the data for custom PII categories.
B. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
C. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.
D. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.

Answer: B

NEW QUESTION # 367
A company must retain specific data for 1 year. A data engineer observes that one of the company's Amazon S3 buckets contains millions of objects that are older than 3 years.
Versioning is enabled on the bucket.
To reduce costs, the data engineer implements an S3 Lifecycle rule to expire objects after 365 days. The new S3 Lifecycle rule causes the object count to double instead of decrease.
Which additional step must the data engineer take to permanently delete the old objects?

A. Add an additional S3 Lifecycle rule to delete the current and expired versions of objects that are older than 365 days.
B. Disable versioning on the S3 bucket.
C. Use an AWS Lambda function to run a Python job to identify and delete objects that are older than 365 days.
D. Suspend versioning on the S3 bucket.

Answer: A

Explanation:
With S3 versioning enabled, lifecycle expiration of an object typically adds a delete marker instead of removing prior versions, which can increase the total number of object versions. To permanently remove the older data, the lifecycle configuration must also delete noncurrent (previous) versions and expired delete markers based on age so that both current and prior versions are actually removed.

NEW QUESTION # 368
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

A. Bytes sent over the wireframe - amount of data sent over the wireframe
B. Bytes read from result - bytes read from the result object.
C. Bytes written - bytes written (e.g. when loading into a table).
D. Percentage scanned from cache - the percentage of data scanned from the local disk cache.
E. External bytes scanned - bytes read from an external object, e.g. a stage.

Answer: A

Explanation:
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

NEW QUESTION # 369
A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

A. Set the DynamicFrame additional_options parameter `useS3ListImplementation' to True.
B. Create a schema definition for PySpark that matches the format the processing workflow requires for postal codes. Pass the schema to the DynamicFrame during processing.
C. Configure the column.push_down_predicate setting and the catalogPartitionPredicate settings for the postal code column in the DynamicFrame.
D. Use AWS Glue workflow properties to allow job state sharing. Configure the AWS Glue jobs to read values from the postal code column by using the properties from a previously successful run of the jobs.

Answer: B

Explanation:
By defining and enforcing a strict schema in PySpark that specifies the exact format you expect for postal codes (for example, a string of five digits), Spark will automatically flag or coerce any values that don't match. Passing that schema into your Glue DynamicFrame ensures that malformed postal codes are identified and can be corrected or filtered out as part of your ETL logic, eliminating the downstream errors.

NEW QUESTION # 370
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