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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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 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 4	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 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 (Q102-Q107):

NEW QUESTION # 102
A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services.
Which solution will meet these requirements with the LEAST amount of refactoring?

A. Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances.
Update the network connections to interact with the newly deployed EC2 instances.
B. Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.
C. Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
D. Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.

Answer: B

Explanation:
Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.

NEW QUESTION # 103
Melissa, Senior Data Engineer, looking out to optimize query performance for one of the Critical Control Dashboard, she found that most of the searches by the users on the control dashboards are based on Equality search on all the underlying columns mostly. Which Best techniques she should consider here?

A. Melissa can create Indexes & Hints on the searchable columns to speed up Equality search.
B. The search optimization service would best fit here as it can be applied to all underlying columns & speeds up equality searches.
(Correct)
C. She can go for clustering on underlying tables which can speedup Equality searches.
D. A materialized view speeds both equality searches and range searches.

Answer: B

Explanation:
Explanation
Clustering a table can speed any of the following, as long as they are on the clustering key:
Range searches.
Equality searches.
However, a table can be clustered on only a single key (which can contain one or more columns or expressions).
The search optimization service speeds equality searches. However, this applies to all the columns of supported types in a table that has search optimization enabled. This is what required here& best fit for purpose.
A materialized view speeds both equality searches and range searches, as well as some sort opera-tions, but only for the subset of rows and columns included in the materialized view.

NEW QUESTION # 104
A Data Engineer wants to check the status of a pipe named my_pipe. The pipe is inside a database named test and a schema named Extract (case-sensitive).
Which querywill provide the status of the pipe?

A. SELE2T * FROM SYSTEM$PIPE_STATUS < ' test. "Extract", my_pipe');
B. SELECT * FROM SYSTEM$PIPE_STATUS ("test. 'extract' .my_pipe"};
C. SELECT FROM SYSTEM$PIPE_STATUS (,test.,,Extracr,,.ny_pipe, i I
D. SELECT FROM SYSTEM$PIPE_STATUS (''test.'extract'.my_pipe"i:

Answer: A

Explanation:
Explanation
The query that will provide the status of the pipe is SELECT * FROM
SYSTEM$PIPE_STATUS('test."Extract".my_pipe');. The SYSTEM$PIPE_STATUS function returns information about a pipe, such as its name, status, last received message timestamp, etc. The function takes one argument: the pipe name in a qualified form. The pipe name should include the database name, the schema name, and the pipe name, separated by dots. If any of these names are case-sensitive identifiers, they should be enclosed in double quotes. In this case, the schema name Extract is case-sensitive and should be quoted. The other options are incorrect because they do not follow the correct syntax for the pipe name argument. Option A and B use single quotes instead of double quotes for case-sensitive identifiers. Option D uses double quotes instead of single quotes for non-case-sensitive identifiers.

NEW QUESTION # 105
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?

A. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
B. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
D. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.

Answer: A

Explanation:
Amazon DynamoDB with on-demand capacity is designed to handle unpredictable, high-volume request patterns and sudden traffic spikes while maintaining single-digit millisecond latency.
Using Product ID as the table's primary key enables efficient item retrieval by the primary identifier, and global secondary indexes on Category and Brand provide flexible, low-latency queries on those secondary attributes.

NEW QUESTION # 106
A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake.
The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data.
The company needs to orchestrate and automate the ETL workflows with minimal manual intervention.
Which solution will meet these requirements?

A. Configure AWS Lambda functions to process Amazon S3 event notifications for data transformation tasks when new data is uploaded.
B. Configure AWS Step Functions and Amazon EventBridge to orchestrate and invoke ETL workflows in AWS Glue and Amazon EMR.
C. Use Amazon Managed Workflows for Apache Airflow automatic scheduling to orchestrate the Spark-based ETL jobs.
D. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline.

Answer: D

Explanation:
AWS Glue provides a fully managed serverless ETL service with native Spark support. By migrating all ETL jobs to Glue, the company eliminates the need to manage Amazon EMR clusters. Glue workflows can orchestrate and automate multiple ETL jobs, ensuring dependencies are respected and executions are automated with minimal manual intervention. This satisfies the requirement for a serverless, Spark-based, fully managed solution.

NEW QUESTION # 107
......

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