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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Real Exam Qty:	approximately 50 questions
Available Languages:	English
Certificate Validity Period:	2 years (recertification required)
Exam Price:	$375 USD
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Exam Duration:	90 minutes
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Exam Format:	Multiple Choice, Multiple Select
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	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	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	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 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	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 (Q269-Q274):

NEW QUESTION # 269
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput.
The solution must use cluster resources optimally when the company loads data into the fact table.
Which solution will meet these requirements?

A. Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.
B. Use a single COPY command to load the data into the Redshift cluster.
C. Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
D. Use multiple COPY commands to load the data into the Redshift cluster.

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/t_Loading-data-from-S3.html

NEW QUESTION # 270
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?

A. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
B. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
D. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.

Answer: B

Explanation:
Amazon DynamoDB with on-demand capacity is designed to handle unpredictable, high-volume request patterns and sudden traffic spikes while maintaining single-digit millisecond latency.
Using Product ID as the table's primary key enables efficient item retrieval by the primary identifier, and global secondary indexes on Category and Brand provide flexible, low-latency queries on those secondary attributes.

NEW QUESTION # 271
Company DEF has a strict security policy that mandates that all data at rest in Amazon S3 must be encrypted. They want to ensure that the encryption keys are managed by AWS, but they also want the flexibility to change the encryption keys when required.
Which of the following encryption methods best meets Company DEF's requirements?

A. Server-Side Encryption with Customer-Provided Keys (SSE-C).
B. Server-Side Encryption with Amazon S3 Managed Keys (SSE-S3).
C. Client-Side Encryption with a client-side master key.
D. Server-Side Encryption with AWS Key Management Service (SSE-KMS).

Answer: D

NEW QUESTION # 272
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between
100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
B. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
C. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command.
Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger.
D. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.

Answer: C

Explanation:
Configuring your S3 bucket to emit "Object Created" events into EventBridge and using a rule to invoke a lightweight Lambda function lets you automatically run a Redshift COPY for each new CSV. This serverless, event-driven pattern requires no continuously running infrastructure and has minimal operational overhead.

NEW QUESTION # 273
A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.
B. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
C. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
D. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.

Answer: C

NEW QUESTION # 274
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