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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q216-Q221):

NEW QUESTION # 216
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

A. Set the FORCE option to load all files, ignoring load metadata if it exists.
B. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
C. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
D. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.

Answer: D

Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files

NEW QUESTION # 217
A company regularly loads data into an Amazon DynamoDB table. The company must retain the data in DynamoDB for 1 year. After 1 year, the company must archive the data for 5 years. After
5 years, the company must delete the data. Which solution will meet these requirements in the MOST operationally efficient way?

A. Include a timestamp with the data that is loaded into DynamoDB. Use an AWS Lambda function to read timestamps and move items that are 1 year old directly to Amazon S3 Glacier Deep Archive. Configure the function to delete items after 5 years.
B. Enable Amazon DynamoDB Streams. Configure a TTL rule to delete items from the DynamoDB table after 1 year. Use an AWS Lambda function to consume the stream and send items to Amazon S3 Glacier Flexible Retrieval. Delete the items from S3 Glacier Flexible Retrieval after 5 years.
C. Include a timestamp with the data that is loaded into DynamoDB. Create an AWS Lambda function to read the timestamps and move items that are 1 year old to Amazon S3 Glacier Flexible Retrieval. Create an S3 Lifecycle policy to delete the items after 5 years.
D. Enable Amazon DynamoDB Streams. Configure a TTL rule to delete items from the DynamoDB table after 1 year. Use an AWS Lambda function to consume the stream and send items directly to Amazon S3 Glacier Deep Archive. Create an S3 Lifecycle policy to delete the items after 5 years.

Answer: D

Explanation:
DynamoDB TTL provides a fully managed way to remove items after 1 year with minimal operational work. DynamoDB Streams can capture the item changes so a Lambda function can archive the expiring data to Amazon S3 before it is removed from the table. Using S3 Glacier Deep Archive minimizes archive storage cost for long retention, and an S3 Lifecycle policy can automatically delete the archived objects after 5 years, avoiding custom deletion logic.

NEW QUESTION # 218
A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?

A. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.
B. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.
C. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.
D. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.

Answer: A

Explanation:
AWS Glue job bookmarks are designed to handle incremental data processing by automatically tracking the state.

NEW QUESTION # 219
Data Engineer is using existing pipe that automates data loads using event notifications, later he figured out the needs to modify pipe properties. For the same, He decided to recreate the pipe as best practice. He followed the below steps for the same.
1. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
2. Recreate the pipe (using CREATE OR REPLACE PIPE).
3. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is RUN-NING.
Which are the Missing recommended steps while Recreating Pipes for Automated Data Loads?

A. Terminate the existing pipe (using ALTER PIPE ... SET PIPE_EXECUTION_TERMINATE = true) before recreation.
B. CREATE OR REPLACE PIPE command will recreate the PIPE successfully.
C. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true) Pre & Post recreation & Resume after recreation (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = false).
D. Force the pipe to resume (using SYSTEM$PIPE_FORCE_RESUME).

Answer: C

Explanation:
Explanation
Recreating a pipe (using a CREATE OR REPLACE PIPE statement) is necessary to modify most pipe properties.
Recreating Pipes for Automated Data Loads
When recreating a pipe that automates data loads using event notifications, it's recommended that Data Engineer complete the following steps:
1. Pause the pipe (using ALTER PIPE ... SET PIPE_EXECUTION_PAUSED = true).
2. Query the SYSTEM$PIPE_STATUS function and verify that the pipe execution state is PAUSED.
3. Recreate the pipe (using CREATE OR REPLACE PIPE).
4. Pause the pipe again.
5. Review the configuration steps for your cloud messaging service to ensure the settings are still accurate.
6. Query the SYSTEM$PIPE_STATUS function again and verify that the pipe execution state is RUNNING.

NEW QUESTION # 220
Within a Snowflake account permissions have been defined with custom roles and role hierarchies.
To set up column-level masking using a role in the hierarchy of the current user, what command would be used?

A. IS_RCLE_IN_SESSION
B. IS_GRANTED_TO_INVOKER_ROLE
C. IKVOKER_ROLE
D. CORRECT_ROLE

Answer: A

Explanation:
Explanation
The IS_ROLE_IN_SESSION function is used to set up column-level masking using a role in the hierarchy of the current user. Column-level masking is a feature in Snowflake that allows users to apply dynamic data masking policies to specific columns based on the roles of the users who access them. The IS_ROLE_IN_SESSION function takes a role name as an argument and returns true if the role is in the current user's session, or false otherwise. The function can be used in a masking policy expression to determine whether to mask or unmask a column value based on the role of the user. For example:
CREATE OR REPLACE MASKING POLICY email_mask AS (val string) RETURNS string -> CASE WHEN IS_ROLE_IN_SESSION('HR') THEN val ELSE REGEXP_REPLACE(val, '(.).(.@.)', '\1****\2') END; In this example, the IS_ROLE_IN_SESSION function is used to create a masking policy for an email column.
The masking policy returns the original email value if the user has the HR role in their session, or returns a masked email value with asterisks if not.

NEW QUESTION # 221
......

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