DEA-C01 New Questions | DEA-C01 Exam Syllabus

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 2
  • Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 3
  • Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 4
  • Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 5
  • Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
A data engineer must orchestrate a series of Amazon Athena queries that will run every day.
Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

Answer: B,D


NEW QUESTION # 54
A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data.
The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)

Answer: A,D

Explanation:
Choice A ensures that live transactional data from PostgreSQL can be accessed directly within Redshift queries.
Choice C archives historical data in Amazon S3, reducing storage costs in Redshift while still making the data accessible via Redshift Spectrum.


NEW QUESTION # 55
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?

Answer: B

Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
When created, a stream logically takes an initial snapshot of every row in the source object (e.g. ta-ble, external table, or the underlying tables for a view) by initializing a point in time (called an off-set) as the current transactional version of the object. The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken. Change rec-ords provide the state of a row before and after the change. Change information mirrors the column structure of the tracked source object and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.


NEW QUESTION # 56
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?

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 # 57
A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke.
When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.
How should the data engineer resolve the exception?

Answer: B

Explanation:
The lambda resource based policy must allow the events principle to invoke the lambda function.
Amazon SQS, Amazon SNS, Lambda, CloudWatch Logs, and EventBridge bus targets do not use roles, and permissions to EventBridge must be granted via a resource policy.
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule


NEW QUESTION # 58
......

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