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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q177-Q182):

NEW QUESTION # 177
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads. Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon SageMaker Processing jobs to run PySpark code that uses native SageMaker libraries.
B. Use AWS Glue Studio to build and schedule PySpark jobs. Configure an AWS Glue data connection that includes the custom libraries.
C. Use Amazon EMR to run PySpark jobs. Use bootstrap actions to install the custom libraries
D. Use Amazon EC2 Auto Scaling groups with a custom AMI that contains the custom libraries to run a PySpark application.

Answer: B

Explanation:
AWS Glue is a fully managed, highly scalable serverless service for running PySpark ETL jobs, and it supports adding custom Python libraries to Glue jobs from Amazon S3 by using job parameters such as --additional-python-modules and --extra-py-files. AWS Glue can also write to Amazon Redshift, making it the best fit for daily large-scale PySpark transformations with the least operational overhead compared with managing EC2 instances or EMR clusters.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-python-libraries.html
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-connect-redshift- home.html

NEW QUESTION # 178
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?

A. CREATE TABLE new_table
AS SELECT *
FROM old_table;
B. CREATE TABLE new_table
AS SELECT *
FROM old_table
WITH NO DATA;
C. CREATE TABLE new_table
LIKE old_table;
D. CREATE TABLE new_table
as SELECT *
FROM old_cable
WHERE 1=1;

Answer: B

NEW QUESTION # 179
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?

A. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
B. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
C. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
D. AWS DMS only supports full-load migrations, which would require downtime for the source database.
E. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.

Answer: E

NEW QUESTION # 180
Stuart, a Lead Data Engineer in MACRO Data Company created streams on set of External tables. He has been asked to extend the data retention period of the stream for 90 days, which parameter he can utilize to enable this extension?

A. MAX_DATA_EXTENSION_TIME_IN_DAYS
B. None of the above
C. DATA_EXTENSION_TIME_IN_DAYS
D. DATA_RETENTION_TIME_IN_DAYS

Answer: B

Explanation:
Explanation
External tables do not have data retention period applicable.
Good to Understand other Options available.
DATA_RETENTION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Number of days for which Snowflake retains historical data for performing Time Trav-el actions (SELECT, CLONE, UNDROP) on the object. A value of 0 effectively disables Time Travel for the specified database, schema, or table.
Values:
0 or 1 (for Standard Edition)
0 to 90 (for Enterprise Edition or higher)
Default:
1
MAX_DATA_EXTENSION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Maximum number of days for which Snowflake can extend the data retention period for tables to prevent streams on the tables from becoming stale. By default, if the DA-TA_RETENTION_TIME_IN_DAYS setting for a source table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to the stream's offset, up to a maximum of 14 days, regardless of the Snowflake Edition for your account. The MAX_DATA_EXTENSION_TIME_IN_DAYS parameter enables you to limit this automatic ex-tension period to control storage costs for data retention or for compliance reasons.
This parameter can be set at the account, database, schema, and table levels. Note that setting the parameter at the account or schema level only affects tables for which the parameter has not already been explicitly set at a lower level (e.g. at the table level by the table owner). A value of 0 effective-ly disables the automatic extension for the specified database, schema, or table.
Values:
0 to 90 (i.e. 90 days) - a value of 0 disables the automatic extension of the data retention period. To increase the maximum value for tables in your account, Client needs to contact Snowflake Sup-port.
Default:14

NEW QUESTION # 181
A retail company wants to implement real-time analytics for an ecommerce platform. The company needs to collect clickstream data from the company's website and mobile apps. The company needs to store the data for analytics. Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Use an Amazon Managed Streaming for Apache Kafka (Amazon MSK) broker to collect the data.Store the data in an Amazon RDS DB instance.
B. Deploy agents on Amazon EC2 instances to collect the streaming data. Use the AWS CLI to periodically batch upload the data to an Amazon S3 bucket.
C. Use Amazon Kinesis Data Streams to collect the streaming data. Use Amazon Data Firehose to deliver the data to an Amazon S3 bucket.
D. Use Amazon Data Firehose to ingest the streaming data. Deliver the processed data directly to a provisioned Amazon Redshift cluster.

Answer: C

Explanation:
Amazon Kinesis Data Streams is well suited to collect high-volume real-time clickstream data from websites and mobile apps, and Amazon Data Firehose can then deliver that streaming data to Amazon S3 with very little operational effort. This approach is highly managed and avoids the ongoing administration required for EC2-based collectors, self-managed Kafka patterns, or maintaining a provisioned Redshift cluster for raw landing storage.
Reference:
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
https://docs.aws.amazon.com/firehose/latest/dev/basic-deliver.html

NEW QUESTION # 182
......

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