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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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 (Q309-Q314):

NEW QUESTION # 309
The following is returned fromSYSTEMCLUSTERING_INFORMATION () for a tablenamed orders with adate column named O_ORDERDATE:

What does the total_constant_partition_count value indicate about this table?

A. The data inO_ORDERDATEhas a very low cardinality as there are 493 micro-partitions where there is only a single distinct value in that column for all rows in the micro-partition
B. The table is not clustered well on O_ORDERDATE, as there are 493 micro-partitions where the range of values in that column overlap with every other micro partition in the table.
C. The data inO_ORDERDATEdoes not change very often as there are 493 micro-partitionscontaining rows where that column has not been modified since the row was created
D. The table is clustered very well on_ORDERDATE, as there are 493 micro-partitions that could not be significantly improved by reclustering

Answer: B

Explanation:
Explanation
The total_constant_partition_count value indicates the number of micro-partitions where the clustering key column has a constant value across all rows in the micro-partition. However, this does not necessarily mean that the table is clustered well on that column, as there could be other micro-partitions where the range of values in that column overlap with each other. This is the case for the orders table, as the clustering depth is 1, which means that every micro-partition overlaps with every other micro-partition on O_ORDERDATE. This indicates that the table is not clustered well on O_ORDERDATE and could benefit from reclustering.

NEW QUESTION # 310
A company is using Amazon Redshift as a data warehousing service. It is running analytical queries on large amounts of data using ODBC connections. The operations team is observing that some queries are getting completed while others are getting hung. They are seeking your suggestions to resolve this issue.
What setting can be done?

A. Set a higher fetch size per query.
B. Reduce the size of MTU (Maximum Transmission Unit).
C. Increase the size of MTU (Maximum Transmission Unit) to jumbo size.
D. Set a lower fetch size per query.

Answer: B

Explanation:
Maximum Transmission Unit (MTU) determines the maximum size in packet bytes that can be transferred in a single Ethernet frame between two hosts.
Customers may face query hang issues when the MTU size in a network path between two IP hosts is mismatched. To resolve the query hang issue for Redshift ODBC connections, the MTU size can be reduced.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/queries-troubleshooting.html#queries-troubleshooting-query-hangs
https://docs.aws.amazon.com/redshift/latest/mgmt/connecting-drop-issues.html

NEW QUESTION # 311
Which of the following security and governance tools/technologies are known to provide native connectivity to Snowflake? [Select 2]

A. Zepl
B. BIG Squid
C. Baffle
D. ALTR
E. Dataiku

Answer: C,D

Explanation:
Explanation
Security and governance tools ensure sensitive data maintained by an organization is protected from inappropriate access and tampering, as well as helping organizations to achieve and maintain regula-tory compliance. These tools are often used in conjunction with observability solutions/services to provide organizations with visibility into the status, quality, and integrity of their data, including identifying potential issues.
Together, these tools support a wide range of operations, including risk assessment, intrusion detec-tion/monitoring/notification, data masking, data cataloging, data health/quality checks, issue identi-fication/troubleshooting/resolution, and more.
ALTR & Baffle are correct options here.

NEW QUESTION # 312
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?

A. Create a document that includes the data preparation rules. Ensure that the data engineers implement the rules in AWS Glue.
B. Use Python and Pandas in an AWS Glue Studio notebook. Ensure that the data engineers add additional transformations to complete the pipeline.
C. Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data engineers add additional transformations to complete the pipeline.
D. Use Amazon SageMaker Canvas and SageMaker Data Wrangler to write to a new dataset.
Ensure that the data engineers add additional transformations to complete the pipeline by using AWS Glue.

Answer: C

Explanation:
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.

NEW QUESTION # 313
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?

A. TRUE
B. FALSE

Answer: A

Explanation:
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.

NEW QUESTION # 314
......

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