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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q256-Q261):

NEW QUESTION # 256
David, a Lead Data engineer with XYZ company looking out to improve query performance & oth-er benefits while working with Tables, Regular Views, MVs and Cached Results.
Which one of the following does not shows key similarities and differences between tables, regular views, cached query results, and materialized views while choosing any of them by David?

A. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
B. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.
C. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
D. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
E. Materialized views are faster than tables because of their "cache" (i.e. the query results for the view); in addition, if data has changed, they can use their "cache" for data that hasn't changed and use the base table for any data that has changed.

Answer: B

Explanation:
Explanation
Materialized Views, like other database objects (tables, views, UDFs, etc.), are owned by a role and have privileges that can be granted to other roles.
You can grant the following privileges on a materialized view:
SELECT
As with non-materialized views, a materialized view does not automatically inherit the privileges of its base table. You should explicitly grant privileges on the materialized view to the roles that should use that view.
As with non-materialized views, a user who wishes to access a materialized view needs privileges only on the view, not on the underlying object(s) that the view references.
Rest is correct.

NEW QUESTION # 257
External Function is a type of UDF & can be Scaler or Tabular?

A. FALSE
B. TRUE

Answer: A

Explanation:
Explanation
External functions must be scalar functions. A scalar external function returns a single value for each input row.

NEW QUESTION # 258
When created, a stream logically takes an initial snapshot of every row in the source object and the contents of a stream change as DML statements execute on the source table.
A Data Engineer, Sophie Created a view that queries the table and returns the CURRENT_USER and CURRENT_TIMESTAMP values for the query transaction. A Stream has been created on views to capture CDC.
Tony, another user inserted the data e.g.
insert into <table> values (1),(2),(3);
Emily, another user also inserted the data e.g.
insert into <table> values (4),(5),(6);
What will happened when Different user queries the same stream after 1 hour?

A. User would be displayed with the one who queried during the session, but Recorded timestamp would be of past 1 hour i.e. actual records insertion time.
B. All the 6 records would be shown with METADATA$ACTION as 'INSERT' out of which 3 records would be displayed with username 'Tony' & rest 3 records would be displayed with username 'Emily'.
C. All the Six records would be displayed with User 'Sohpie' Who is the owner of the View.
D. All the Six Records would be displayed with CURRENT_USER & CUR-RENT_TIMESTAMP while querying Streams.

Answer: D

Explanation:
Explanation
When User queries the stream, the stream returns the username for the user. The stream also returns the current timestamp for the query transaction in each row, NOT the timestamp when each row was inserted.

NEW QUESTION # 259
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

A. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
B. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.
C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

Answer: D

Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/logging-with-S3.html

NEW QUESTION # 260
A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

A. Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster.
Write the logs to a table in the Aurora MySQL database.
B. Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.
C. Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.
D. Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.

Answer: B

Explanation:
Amazon Redshift provides an option to log and store all user activities and connection activities for compliance and auditing purposes. Configuring logging to an Amazon S3 bucket is the recommended approach because:
Amazon S3 is cost-effective, scalable, and durable for storing large volumes of logs over time.
Amazon Redshift natively supports logging to S3, allowing user and connection logs to be easily enabled and stored directly in the specified S3 bucket.
Storing logs in S3 meets compliance needs and provides an efficient way to manage, archive, and retrieve logs for security reviews or audits.

NEW QUESTION # 261
......

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