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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 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	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.
Topic 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q369-Q374):

NEW QUESTION # 369
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

A. Transition the data to Amazon S3 Glacier Flexible Retrieval.
B. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.
C. Transition the data from Amazon S3 to an Amazon Aurora cluster.
D. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.

Answer: B

Explanation:
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.

NEW QUESTION # 370
Michael, a Data Engineer Running a Data query to achieve Union of Data sets coming from Multi-ple data sources, later he figured out that Data processing query is taking more time than expected. He started analyzing the Query performance using query profile interface. He discovered & realized that he used UNION when the UNION ALL semantics was sufficient.
Which Extra Data Processing Operator Michael figured out while doing query profile analysis in this case which helps him to identify this performance bottlenecks?

A. Join
B. UNION ALL
C. Aggregate
D. Filter
E. Flatten

Answer: C

Explanation:
Explanation
In SQL, it is possible to combine two sets of data with either UNION or UNION ALL constructs. The difference between them is that UNION ALL simply concatenates inputs, while UNION does the same, but also performs duplicate elimination.
A common mistake is to use UNION when the UNION ALL semantics are sufficient. These que-ries show in Query Profile as a UnionAll operator with an extra Aggregate operator on top (which performs duplicate elimination).
To Know more about Data Processing Operators, please do refer:
https://docs.snowflake.com/en/user-guide/ui-query-profile#operator-types

NEW QUESTION # 371
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?

A. AWS Glue
B. Amazon EventBridge
C. AWS Step Functions
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: D

Explanation:
Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run Apache Airflow at scale. Airflow is a popular tool for orchestrating complex workflows, including ETL jobs, and can handle various task types such as running Apache Spark jobs on Amazon EMR, making API calls to external systems like Salesforce, and loading data into Amazon Redshift. Airflow allows you to define workflows using Python and offers built-in mechanisms for handling failures, retries, and retries automatically, making it a great fit for this use case.
While AWS Step Functions can orchestrate workflows and handle retries and errors, it is less suited for Python-based orchestration compared to Airflow. Also, Step Functions is more suited for AWS services and does not offer built-in support for external systems like Salesforce, which would require custom integrations.
AWS Glue is a serverless ETL service, but it primarily focuses on data transformation tasks. Glue does not handle orchestration of complex workflows involving non-Glue jobs, such as calling Salesforce APIs or running Spark jobs on EMR, as effectively as Airflow.
EventBridge is an event-driven service, not an orchestration service. It is designed for event- based workflows but does not offer the workflow orchestration capabilities needed for handling tasks like retries, error handling, and running complex workflows across different systems.

NEW QUESTION # 372
Bob, a Lead Data Engineer is looking out to get the function definition & queried below statement to check if this function is secure enough to use in his script or not.
select is_secure from information_schema.functions where function_name = 'JOHNFUNCTION'; From the query output he is sure that, Function is secure UDF, what are the way provided by snow-flake to get the function definition of secure UDF?

A. UDF definition or text, is visible to users via Query Profile (in the web interface).
B. SHOW FUNCTIONS Commands
C. He can get the secure UDF definition using GET_DDL utility function.
D. Declaring a UDF as "secure" hide the definition from Bob & all the required Definition commands will throw error.

Answer: D

NEW QUESTION # 373
Which of the following statements is/are incorrect regarding Fail-safe data recovery?

A. If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.
B. Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.
C. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
D. Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.
E. Historical data in transient tables can be recovered by Snowflake due to Operation fail-ure after the Time Travel retention period ends using Fail-safe.

Answer: E

NEW QUESTION # 374
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