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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Certificate Validity Period:	2 years (recertification required)
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Exam Price:	$375 USD
Real Exam Qty:	approximately 50 questions
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Available Languages:	English
Exam Duration:	90 minutes
Exam Format:	Multiple Select, Multiple Choice
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:	SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:	https://www.snowflake.com/certification/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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	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 3	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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
A company needs to implement real-time analytics for a retail shopping platform. The company wants to capture clickstream data, process the data, and load the data into Amazon Redshift for analysis. The solution must handle hundreds of megabytes of data every second. Which solution will meet these requirements with the LEAST query latency for analytics?

A. Use Amazon DynamoDB Streams to capture the data. Use AWS Glue to process the data. Use a zero-ETL integration to load the data into Amazon Redshift.
B. Use Amazon Kinesis Data Streams to capture the data. Use Amazon Redshift streaming ingestion to load data directly into materialized views.
C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to capture the data. Use Amazon EMR to process the data. Use federated queries to access data in Amazon Redshift.
D. Use Amazon Data Firehose to capture the data. Store the data in an Amazon S3 bucket. Use the COPY command to load data into Amazon Redshift.

Answer: B

Explanation:
Amazon Kinesis Data Streams with Amazon Redshift streaming ingestion provides the lowest analytics latency because Amazon Redshift can ingest streaming data directly into materialized views without first staging the data in Amazon S3. AWS documentation describes Redshift streaming ingestion from Kinesis as low-latency and high-speed, which makes it the best fit for near real-time clickstream analytics at high throughput.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html

NEW QUESTION # 132
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 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.
B. 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.
C. 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.
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: C

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 # 133
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_logswhere eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessageorder by eventname asc 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_logs where eventtime >= '2024-01-01T00:00:00Z' group by eventname, errorcode, errormessage order by TotalEvents desc limit 10;
D. 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;

Answer: D

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 # 134
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

A. Transition the data from Amazon S3 to an Amazon Aurora cluster.
B. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
C. Transition the data to Amazon S3 Glacier Flexible Retrieval.
D. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.

Answer: D

Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.

NEW QUESTION # 135
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions?

A. FALSE
B. TRUE

Answer: B

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure views that will be shared to other Snowflake accounts, Snowflake returns a NULL value for these functions. The reason is that the owner of the data being shared does not typically control the users or roles in the account with which the view is being shared.

NEW QUESTION # 136
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