We're so confident of our products that we provide no hassle product exchange.
Snowflake DEA-C02 Exam Overview:
| Certification Vendor: | Snowflake |
| Exam Name: | SnowPro® Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C02 |
| Available Languages: | English, Japanese |
| Exam Duration: | 115 minutes |
| Exam Format: | Multiple choice, Multiple select, Scenario-based questions |
| Certificate Validity Period: | 2 years |
| Passing Score: | 750 (scaled score, range 0-1000) |
| Exam Price: | $375 USD |
| Related Certifications: | SnowPro Core SnowPro Advanced: Architect SnowPro Advanced: Administrator |
| Real Exam Qty: | 65 |
| Recommended Training: | Snowflake Instructor-Led: Data Engineering Workshop SnowPro Advanced: Data Engineer Official Study Guide |
| Exam Registration: | Pearson VUE Registration Snowflake Certification Portal |
| Sample Questions: | Snowflake DEA-C02 Sample Questions |
| Exam Way: | Online proctored (OnVUE) or onsite at Pearson VUE test centers |
| Pre Condition: | Recommended: Active SnowPro Core certification; 2+ years hands-on data engineering experience with Snowflake |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer-C02/ |
Snowflake DEA-C02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization and Compute Management | 15-20% | - Monitor and tune workloads and resource utilization - Optimize query performance: clustering, partitioning, materialized views - Use search optimization and query acceleration services - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control |
| Data Transformation and Processing | 20-25% | - Transform and enrich data using SQL, JavaScript, Python, and Snowpark - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables - Process semi-structured data: JSON, Avro, Parquet, ORC - Manage data quality, validation, and deduplication |
| Data Sharing and Collaboration | 5-10% | - Implement secure data sharing and data exchanges - Work with Snowflake Data Marketplace and external data providers - Design multi-tenant and cross-account data architectures |
| Data Governance, Security, and Compliance | 10-15% | - Enforce data quality and governance standards - Manage data lineage, cataloging, and compliance policies - Implement access control: RBAC, authentication, authorization - Apply data protection: encryption, masking, row-level security |
| Data Pipeline Architecture and Design | 15-20% | - Design scalable, reliable, and maintainable data pipelines - Apply design patterns for data engineering workloads - Integrate with external tools and platforms: orchestration, BI, ML - Build end-to-end near real-time streaming solutions |
| Data Ingestion and Sourcing | 20-25% | - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Handle different data formats: structured, semi-structured, unstructured - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions - Design and implement continuous and batch ingestion pipelines |
Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:
1. You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?
A) Option B
B) Option E
C) Option D
D) Option A
E) Option C
2. A data engineer is using the Snowflake Spark connector to write data to a Snowflake table. The write operation fails consistently with the error 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: String '. ' is too long (maximum is 16777216)'. Which of the following is the most likely cause and how can it be resolved using Spark Connector?
A) Option B
B) Option E
C) Option D
D) Option A
E) Option C
3. A data engineer observes that a daily data transformation pipeline in Snowflake, which processes data from external stage 's3://my- bucket/raw_dataP , is consistently taking longer to complete. Upon investigation, the engineer finds that the COPY INTO statement is the bottleneck. The COPY INTO statement is as follows:
Which of the following could be the root cause of the performance degradation and how would you address them? Select two options.
A) Snowflake automatically optimizes COPY INTO operations. No specific action is needed.
B) The external stage contains a large number of small files. Snowflake's COPY INTO statement performs best with fewer, larger files. Consolidate the small files into larger files before loading.
C) The 'ON_ERROR = 'CONTINUE" option is causing the COPY INTO statement to perform additional error handling, slowing down the process. Remove the 'ON ERROR clause to improve performance.
D) The PATTERN '. .csv' is inefficient. Refine the PATTERN to be more specific, targeting only the necessary files, potentially using date-based partitioning. If date partitioning is used, leverage partition pruning by including the appropriate date criteria in the COPY INTO statement.
E) The virtual warehouse used for the COPY INTO operation is undersized. Increase the virtual warehouse size to improve performance.
4. You need to implement a data masking policy on the 'EMAIL' column of the 'EMPLOYEES' table. The requirement is to redact the entire email address with 'XXXXX' if the user's role is 'PUBLIC'. If the user's role is 'ANALYST', the domain part of the email should be visible, but the username should be redacted. For all other roles, the full email should be visible. Which of the following SQL statements CORRECTLY implements this masking policy?
A) Option B
B) Option E
C) Option D
D) Option A
E) Option C
5. You are designing a data loading process for a high-volume streaming data source. The data arrives as Avro files in an AWS S3 bucket. You need to load this data into a Snowflake table with minimal latency and operational overhead. Which of the following combinations of Snowflake features and configurations would be MOST suitable for this scenario? (Select TWO)
A) Configure an external table pointing to the S3 bucket and query the Avro files directly from Snowflake.
B) Use a Kafka connector to stream data directly from the Kafka topic to Snowflake.
C) Create a custom Spark application that reads Avro files from S3, transforms the data, and then writes it to Snowflake using the Snowflake Spark connector.
D) Implement Snowpipe with auto-ingest configured to listen for S3 event notifications whenever a new Avro file is added to the bucket.
E) Use the 'COPY INTO' command with a scheduled task that runs every 5 minutes to load new files from the S3 bucket.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B,D | Question # 4 Answer: B | Question # 5 Answer: B,D |
By Elva
