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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Governance and Security	15-20%	- Access Control 1. Role-based access control (RBAC) 2. GRANT and REVOKE operations 3. Row-level and column-level security 4. Role hierarchy and ownership - Data Protection 1. Data re-identification risks 2. Secure data sharing across accounts 3. Data masking and tokenization 4. External tokenization - Compliance and Monitoring 1. Access history and audit trails 2. Tag-based policies 3. Data classification
Topic 2: Data Ingestion	15-20%	- Bulk Data Loading 1. Data loading error handling and validation 2. File format considerations (CSV, JSON, Parquet, Avro) 3. Staging configurations (internal and external stages) 4. COPY INTO command options and best practices - Continuous Data Ingestion 1. Serverless compute for Snowpipe 2. Real-time data ingestion patterns 3. Snowpipe configuration and usage
Topic 3: Advanced Snowflake Features	10-15%	- External Integrations 1. Partner integrations (Spark, Kafka, etc.) 2. Snowflake Connector for Python/Java 3. External functions and UDFs - Data Sharing 1. Direct sharing vs Data Marketplace 2. Reader accounts 3. Secure share creation and management - Data Lakes 1. Querying semi-structured data (VARIANT, ARRAY, OBJECT) 2. Snowflake as a Data Lake 3. External tables and Iceberg support
Topic 4: Data Storage and Retrieval	15-20%	- Snowflake Architecture 1. Hybrid and Hyper tables 2. Clustering strategies and key selection 3. Table types (permanent, transient, temporary) 4. Micro-partitioning concepts - Performance Optimization 1. Materialized views and cached results 2. Result set caching 3. Warehouse scaling policies 4. Query profiling and execution plans
Topic 5: Performance Engineering	10-15%	- Query Optimization 1. Query rewrite and optimization techniques 2. Join elimination and simplification 3. Predicate pushdown optimization - Warehouse Management 1. Multi-cluster warehouse configuration 2. Auto-suspend and auto-resume settings 3. Resource monitors and cost control
Topic 6: Data Transformation	20-25%	- SQL-Based Transformations 1. Merge, update, and delete operations 2. Data type conversions and casting 3. Window functions for advanced analytics 4. Complex JOIN operations and optimizations - Data Processing Patterns 1. ELT vs ETL approaches in Snowflake 2. Zero Copy Cloning for data provisioning 3. Time Travel for historical data access - Streams and Tasks 1. Task graph design and dependencies 2. Scheduled and event-based task execution 3. Serverless task warehouse configuration 4. Change Data Capture (CDC) with streams

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DEA-C01 Test Preparation: SnowPro Advanced: Data Engineer Certification Exam & DEA-C01 Exam Lab Questions

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
A Data Engineer needs to ingest invoice data in PDF format into Snowflake so that the data can be queried and used in a forecasting solution.
..... recommended way to ingest this data?

A. Create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data
B. Use Snowpipe to ingest the files that land in an external stage into a Snowflake table
C. Use a COPY INTO command to ingest the PDF files in an external stage into a Snowflake table with a VARIANT column.
D. Create an external table on the PDF files that are stored in a stage and parse the data nto structured data

Answer: A

Explanation:
Explanation
The recommended way to ingest invoice data in PDF format into Snowflake is to create a Java User-Defined Function (UDF) that leverages Java-based PDF parser libraries to parse PDF data into structured data. This option allows for more flexibility and control over how the PDF data is extracted and transformed. The other options are not suitable for ingesting PDF data into Snowflake. Option A and B are incorrect because Snowpipe and COPY INTO commands can only ingest files that are in supported file formats, such as CSV, JSON, XML, etc. PDF files are not supported by Snowflake and will cause errors or unexpected results.
Option C is incorrect because external tables can only query files that are in supported file formats as well.
PDF files cannot be parsed by external tables and will cause errors or unexpected results.

NEW QUESTION # 219
Mark the Correct Statements:
Statement 1. Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database.
Statement 2. Data Engineer can use zero-copy cloning feature for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).

A. Statement 2
B. Both are False.
C. Statement 1 & 2 are correct.
D. Statement 1

Answer: B

Explanation:
Explanation
Snowflake's zero-copy cloning feature provides a convenient way to quickly take a "snapshot" of any table, schema, or database and create a derived copy of that object which initially shares the underlying storage. This can be extremely useful for creating instant backups that do not incur any additional costs (until changes are made to the cloned object).
For example, when a clone is created of a table, the clone utilizes no data storage because it shares all the existing micro-partitions of the original table at the time it was cloned; however, rows can then be added, deleted, or updated in the clone independently from the original table. Each change to the clone results in new micro-partitions that are owned exclusively by the clone and are protect-ed through CDP.

NEW QUESTION # 220
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?

A. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
B. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
C. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.
D. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
E. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.

Answer: E

Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.

NEW QUESTION # 221
A company is designing a serverless data processing workflow in AWS Step Functions that involves multiple steps. The processing workflow ingests data from an external API, transforms the data by using multiple AWS Lambda functions, and loads the transformed data into Amazon DynamoDB.
The company needs the workflow to perform specific steps based on the content of the incoming data.
Which Step Functions state type should the company use to meet this requirement?

A. Task
B. Map
C. Choice
D. Parallel

Answer: C

Explanation:
The Choice state in AWS Step Functions is designed to enable conditional branching within a workflow. It allows the workflow to evaluate data and make decisions based on specific conditions. This makes it ideal for cases where certain steps in the workflow need to be executed based on the content of the incoming data.

NEW QUESTION # 222
A company utilizes AWS Glue for ETL (Extract, Transform, Load) jobs to process large volumes of data stored in Amazon S3. Recently, they have been experiencing performance issues with their Glue jobs, resulting in longer processing times and delays in data delivery. Upon investigation, they found that the bottleneck occurred during data extraction from S3. Which of the following could be the potential cause of this performance issue?

A. The underlying network bandwidth between AWS Glue and Amazon S3 is insufficient, causing slow data transfer rates and impacting job performance.
B. There are issues with the IAM (Identity and Access Management) roles associated with the Glue job, leading to permission errors or delays in accessing S3 data.
C. The Glue job's configuration lacks optimization for parallelism, causing inefficient data extraction from S3, leading to longer processing times.
D. The size and structure of the S3 data files are causing overhead during data extraction, resulting in longer processing times for Glue jobs.

Answer: C

Explanation:
The bottleneck occurs during data extraction from S3, indicating a potential issue with the Glue job's configuration in this scenario. If the job is not optimized for parallelism, it may not efficiently utilize available resources, leading to slower data extraction. Optimizing the Glue job's configuration to enable parallel processing can improve the efficiency of data extraction from S3, reducing processing times.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/optimize-spark-etl-job-performance.html

NEW QUESTION # 223
......

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