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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Loading and Integration	- Snowpipe and continuous ingestion 1. Automated ingestion workflows 2. Event-driven loading - Bulk data loading 1. File format handling 2. COPY INTO command usage
Topic 2: Semi-Structured and Advanced Data Handling	- Semi-structured data processing 1. VARIANT data type usage 2. JSON, Avro, Parquet handling - Advanced transformation techniques 1. Flattening nested data 2. Complex transformations in SQL
Topic 3: Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Storage and compute separation 2. Virtual warehouses and compute scaling - Data ingestion concepts 1. Batch loading strategies 2. Continuous ingestion concepts
Topic 4: Data Pipelines and Transformation	- Streams and Tasks 1. Automating pipelines with Tasks 2. Change Data Capture (CDC) with Streams - ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts
Topic 5: Performance Optimization	- Warehouse optimization 1. Scaling strategies 2. Concurrency management - Query performance tuning 1. Caching mechanisms 2. Clustering and micro-partitions
Topic 6: Security, Governance, and Data Sharing	- Data governance 1. Secure data sharing concepts 2. Data masking policies - Access control 1. Role-based access control (RBAC) 2. Object-level permissions

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q280-Q285):

NEW QUESTION # 280
Select the correct usage statements with regards to SQL UDF?

A. All of above are correct.
B. The body of a UDF cannot contain DDL statements or any DML statement other than SELECT.
C. You can include only one query expression.
D. When using a query expression in a SQL UDF, do not include a semicolon within the UDF body to terminate the query expression.
E. Scalar functions (UDFs) have a limit of 500 input arguments.

Answer: A

NEW QUESTION # 281
Can the same column be specified in both a Dynamic data masking policy signature and a row ac-cess policy signature at the same time?

A. YES
B. NO

Answer: B

NEW QUESTION # 282
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 AWS Glue Studio to build and schedule PySpark jobs. Configure an AWS Glue data connection that includes the custom libraries.
B. Use Amazon EMR to run PySpark jobs. Use bootstrap actions to install the custom libraries
C. Use Amazon SageMaker Processing jobs to run PySpark code that uses native SageMaker libraries.
D. Use Amazon EC2 Auto Scaling groups with a custom AMI that contains the custom libraries to run a PySpark application.

Answer: A

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 # 283
Data Engineer is performing below steps in sequence while working on Stream s1 created on table t1.
Step 1: Begin transaction.
Step 2: Query stream s1 on table t1.
Step 3: Update rows in table t1.
Step 4: Query stream s1.
Step 5: Commit transaction.
Step 6: Begin transaction.
Step 7: Query stream s1.
Mark the Incorrect Operational statements:

A. For Step 5, If the stream was consumed in DML statements within the transaction, the stream position advances to the transaction start time.
B. if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the be-ginning time of Transaction 2 and would not see any changes committed by Transac-tion 1.
C. For Step 7, Results do include table changes committed by Transaction 1.
D. For Step 2, The stream returns the change data capture records between the current position to the Transaction 1 start time. If the stream is used in a DML statement, the stream is then locked to avoid changes by concurrent transactions.
E. For Step 4, Returns the CDC data records by streams with updated rows happened in the Step 3 because Streams works in Repeated committed mode in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.

Answer: E

Explanation:
Explanation
Streams support repeatable read isolation. In repeatable read mode, multiple SQL statements within a transaction see the same set of records in a stream. This differs from the read committed mode supported for tables, in which statements see any changes made by previous statements executed within the same transaction, even though those changes are not yet committed.
The delta records returned by streams in a transaction is the range from the current position of the stream until the transaction start time. The stream position advances to the transaction start time if the transaction commits; otherwise, it stays at the same position.
Within Transaction 1, all queries to stream s1 see the same set of records. DML changes to table t1 are recorded to the stream only when the transaction is committed.
In Transaction 2, queries to the stream see the changes recorded to the table in Transaction 1. Note that if Transaction 2 had begun before Transaction 1 was committed, queries to the stream would have returned a snapshot of the stream from the position of the stream to the beginning time of Transaction 2 and would not see any changes committed by Transaction 1.

NEW QUESTION # 284
A company is using Amazon S3 to build a data lake. The company needs to replicate records from multiple source databases into Apache Parquet format.
Most of the source databases are hosted on Amazon RDS. However, one source database is an on-premises Microsoft SQL Server Enterprise instance. The company needs to implement a solution to replicate existing data from all source databases and all future changes to the target S3 data lake.
Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Database Migration Service (AWS DMS) to replicate existing data and future changes.
B. Use AWS Glue jobs to replicate existing data. Use Amazon Kinesis Data Streams to replicate future changes.
C. Use one AWS Glue job to replicate existing data. Use a second AWS Glue job to replicate future changes.
D. Use AWS Database Migration Service (AWS DMS) to replicate existing data. Use AWS Glue jobs to replicate future changes.

Answer: A

Explanation:
Amazon DMS can perform a one-time full load of each source (including the on-prem SQL Server) and then capture ongoing changes (CDC), delivering them directly to S3 in Apache Parquet format. Using a single DMS replication task per source minimizes infrastructure, operational overhead, and cost.

NEW QUESTION # 285
......

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