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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.

A. The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
B. Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
C. Query results contain results that require significant processing.
D. The view's base table change frequently.

Answer: D

Explanation:
Explanation
A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a material-ized view is faster than executing a query against the base table of the view. This performance dif-ferencecan be significant when a query is run frequently or is sufficiently complex. As a result, ma-terialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views require Enterprise Edition.
Materialized views are designed to improve query performance for workloads composed of com-mon, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough.
Materialized views are particularly useful when:
Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
Query results contain results that require significant processing, including:
1. Analysis of semi-structured data.
2. Aggregates that take a long time to calculate.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
The view's base table does not change frequently.

NEW QUESTION # 248
A data engineer uploads confidential documents to an Amazon S3 bucket every day. The data engineer requires a solution to independently verify the integrity of all uploaded data to confirm that there was no corruption during the transfer process.
Which solution will meet this requirement?

A. Download the complete data after the data is uploaded to the S3 bucket. Programmatically validate the objects for integrity.
B. Change the default encryption on the S3 bucket to server-side encryption with customer-provided keys (SSE-C). Turn on S3 bucket keys to validate data integrity.
C. Calculate the SHA-256 checksum for the objects before uploading the objects. Pass the calculated value to the AWS SDK in each upload request.
D. Download a subset of the data after the data is uploaded to the S3 bucket. Manually validate the objects for integrity.

Answer: C

Explanation:
By calculating a checksum (e.g., SHA-256) before upload and providing it in the upload request, Amazon S3 verifies the checksum on receipt and rejects the upload if the data is corrupted. This provides automatic, independent validation of data integrity without requiring full data downloads.

NEW QUESTION # 249
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

A. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog.
Use data stewards to inspect the data and update the data catalog with the data format.
B. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
C. Use scripts to scan data elements and to assign data classifications based on the format of the data.
D. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

Answer: D

Explanation:
AWS Glue crawlers can automatically scan data in Amazon S3 buckets and Amazon RDS databases to build a data catalog. Glue crawlers also have classifiers that can automatically detect the format of the data (such as CSV, JSON, Parquet, etc.) and store this information as metadata in the Data Catalog. This solution automates the process of cataloging and format recognition, meeting the requirement with the least effort.
The "Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format." option requires manual inspection and updating of the data catalog by data stewards, which adds significant effort and is unnecessary since Glue crawlers can automatically detect the format.
Amazon Macie is primarily used for identifying sensitive data (e.g., PII), not for building a comprehensive data catalog or identifying data formats. It doesn't meet the requirement of cataloging storage format metadata.
Writing custom scripts to scan and classify data based on format is much more labor-intensive compared to using an automated Glue crawler, which handles this task with much less effort.

NEW QUESTION # 250
A Data Engineer ran a stored procedure containing various transactions During the execution, the session abruptly disconnected preventing one transactionfrom committing or rolling hark.The transaction was left in a detached state and created a lock on resources
...must the Engineer take to immediately run a new transaction?

A. Set the LOCK_TIMEOUTto FALSE in the stored procedure
B. Call the system function SYSTEM$CANCEL_TRANSACTION.
C. Set the transaction abort on error to true in the stored procedure.
D. Call the system function SYSTEM$ABORT_TRANSACTION.

Answer: D

Explanation:
Explanation
The system function SYSTEM$ABORT_TRANSACTION can be used to abort a detached transaction that was left in an open state due to a session disconnect or termination. The function takes one argument: the transaction ID of the detached transaction. The function will abort the transaction and release any locks held by it. The other options are incorrect because they do not address the issue of a detached transaction. The system function SYSTEM$CANCEL_TRANSACTION can be used to cancel a running transaction, but not a detached one. The LOCK_TIMEOUT parameter can be used to set a timeout period for acquiring locks on resources, but it does not affect existing locks. The TRANSACTION_ABORT_ON_ERROR parameter can be used to control whether a transaction should abort or continue when an error occurs, but it does not affect detached transactions.

NEW QUESTION # 251
A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an AWS Lambda function to check data quality and to raise exceptions in the code. Run the function when data is added to the S3 bucket. Create an Amazon CloudWatch alarm for exceptions in the code.
B. Create AWS Lambda functions to evaluate data quality rules. Use AWS Step Functions to orchestrate a workflow that publishes notifications when the data fails to meet data quality rules.
C. Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.
D. Create an Amazon EMR provisioned cluster. Add a Python open source data quality package to the EMR cluster. Use the Python package to write code for data quality rules and to copy the data from the S3 bucket to the EMR cluster. Copy the data from the S3 bucket to the EMR cluster. Run the data quality rules.

Answer: C

Explanation:
AWS Glue DataBrew is a fully managed, serverless service with built-in data quality rulesets and profiling jobs that run directly on S3 data. Triggering the profile job via EventBridge on object creation applies rules with minimal code and management overhead.

NEW QUESTION # 252
......

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