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Snowflake DEA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Performance Engineering10-15%- Query Optimization
  • 1. Join elimination and simplification
  • 2. Predicate pushdown optimization
  • 3. Query rewrite and optimization techniques
- Warehouse Management
  • 1. Resource monitors and cost control
  • 2. Auto-suspend and auto-resume settings
  • 3. Multi-cluster warehouse configuration
Data Transformation20-25%- Streams and Tasks
  • 1. Task graph design and dependencies
  • 2. Change Data Capture (CDC) with streams
  • 3. Serverless task warehouse configuration
  • 4. Scheduled and event-based task execution
- SQL-Based Transformations
  • 1. Merge, update, and delete operations
  • 2. Complex JOIN operations and optimizations
  • 3. Window functions for advanced analytics
  • 4. Data type conversions and casting
- Data Processing Patterns
  • 1. ELT vs ETL approaches in Snowflake
  • 2. Time Travel for historical data access
  • 3. Zero Copy Cloning for data provisioning
Data Storage and Retrieval15-20%- Performance Optimization
  • 1. Materialized views and cached results
  • 2. Result set caching
  • 3. Warehouse scaling policies
  • 4. Query profiling and execution plans
- Snowflake Architecture
  • 1. Hybrid and Hyper tables
  • 2. Table types (permanent, transient, temporary)
  • 3. Micro-partitioning concepts
  • 4. Clustering strategies and key selection
Data Governance and Security15-20%- Data Protection
  • 1. Data re-identification risks
  • 2. Secure data sharing across accounts
  • 3. External tokenization
  • 4. Data masking and tokenization
- 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
- Compliance and Monitoring
  • 1. Data classification
  • 2. Access history and audit trails
  • 3. Tag-based policies
Data Ingestion15-20%- Continuous Data Ingestion
  • 1. Snowpipe configuration and usage
  • 2. Serverless compute for Snowpipe
  • 3. Real-time data ingestion patterns
- Bulk Data Loading
  • 1. COPY INTO command options and best practices
  • 2. File format considerations (CSV, JSON, Parquet, Avro)
  • 3. Data loading error handling and validation
  • 4. Staging configurations (internal and external stages)
Advanced Snowflake Features10-15%- Data Lakes
  • 1. Snowflake as a Data Lake
  • 2. Querying semi-structured data (VARIANT, ARRAY, OBJECT)
  • 3. External tables and Iceberg support
- External Integrations
  • 1. External functions and UDFs
  • 2. Partner integrations (Spark, Kafka, etc.)
  • 3. Snowflake Connector for Python/Java
- Data Sharing
  • 1. Secure share creation and management
  • 2. Direct sharing vs Data Marketplace
  • 3. Reader accounts

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

Answer: A,D,E

Explanation:
Explanation
The places where the file format can be defined are copy command, file format object, and stage object. These places allow specifying or referencing a file format that defines how data files are parsed and loaded into or unloaded from Snowflake tables. A file format can include various options, such as field delimiter, field enclosure, compression type, date format, etc. The other options are not places where the file format can be defined. Option B is incorrect because MERGE command is a SQL command that can merge data from one table into another based on a join condition, but it does not involve loading or unloading data files. Option D is incorrect because pipe object is a Snowflake object that can load data from an external stage into a Snowflake table using COPY statements, but it does not define or reference a file format. Option F is incorrect because INSERT command is a SQL command that can insert data into a Snowflake table from literal values or subqueries, but it does not involve loading or unloading data files.


NEW QUESTION # 70
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?

Answer: D

Explanation:
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.


NEW QUESTION # 71
A media streaming company is moving its application to AWS. The company wants to avoid data quality issues in user engagement analytics. The company needs to be able to reprocess several days of historical data without duplicates and without interrupting current real-time processing.
Which solution will meet these requirements?

Answer: D

Explanation:
Extended retention in Amazon Kinesis Data Streams allows the company to keep several days of historical records available for replay instead of being limited to the default 24 hours. To avoid duplicates during both replay and live consumption, consumers should be idempotent because Kinesis processing can deliver duplicate records in retry scenarios. Using DynamoDB for checkpointing is the standard fit with Kinesis consumer processing, and using separate processing paths lets the company replay historical data without disrupting ongoing real-time processing.


NEW QUESTION # 72
A company needs a solution that restricts access to Amazon S3 data and encrypts the data by using AWS managed keys. The solution must manage database credentials that an AWS Lambda function uses and must rotate the credentials automatically. Which solution will meet these requirements?

Answer: B

Explanation:
IAM policies can be used to control access to Amazon S3 data, and server-side encryption with AWS KMS keys (SSE-KMS) supports encryption by using an AWS managed KMS key such as aws/s3. AWS Secrets Manager is the managed service designed to store database credentials and rotate them automatically, including rotation by using a Lambda function. This combination satisfies the requirements for S3 access control, encryption with AWS managed keys, and automatic credential rotation for the Lambda-based application.
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html
https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-policy-language-overview.html
https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html


NEW QUESTION # 73
A company uses a data lake that is based on an Amazon S3 bucket. To comply with regulations, the company must apply two layers of server-side encryption to files that are uploaded to the S3 bucket. The company wants to use an AWS Lambda function to apply the necessary encryption.
Which solution will meet these requirements?

Answer: B

Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingDSSEncryption.html


NEW QUESTION # 74
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