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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 (Q116-Q121):

NEW QUESTION # 116
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. 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.
B. Both materialized views and regular views enhance data security by allowing data to be exposed or hidden at the row level or column level.
C. Cached Query Results: Used only if data has not changed and if query only uses de-terministic functions (e.g. not CURRENT_DATE).
D. Regular views do not cache data, and therefore cannot improve performance by cach-ing.
E. As with non-materialized views, a materialized view automatically inherits the privileges of its base table.

Answer: E

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 # 117
A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
B. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.
C. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.
D. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.

Answer: A

NEW QUESTION # 118
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations, The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account.
The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Choose two.)

A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
B. Add the S3 bucket as a resource that the QuickSight service role can access.
C. Add the KMS key as a resource that the QuickSight service role can access.
D. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the BI-Account account.
E. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.

Answer: C

NEW QUESTION # 119
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?

A. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.1
B. ColumnValues "Email" matches "[%@%.%]" with a threshold set to > 0.9
C. Uniqueness "Email" matches "[%@%.%]" with a threshold set to > 0.9
D. UniqueValueRatio "Email" matches "[%@%.%]" with a threshold set to > 0.1

Answer: B

Explanation:
AWS Glue Data Quality uses the ColumnValues rule with pattern matching to validate that values conform to a required format, such as an email pattern. Setting the threshold to greater than 0.9 ensures that at least 90% of the values in the Email column match the expected email format, causing the rule to fail if data quality drops below this level.

NEW QUESTION # 120
As part of Table Designing, Data Engineer added a timestamp column that inserts the current timestamp as the default value as records are loaded into a table. The intent is to capture the time when eachrecord was loaded into the table; however, the timestamps are earlier than the LOAD_TIME column values returned by COPY_HISTORY view (Account Usage). What could be reason of this issue?

A. It might be possible that Cloud Provider hosted on Snowflake belongs to region having server time zone lagging Cluster time zone of warehouse where queries get processed & committed.
B. The reason is, CURRENT_TIMESTAMP is evaluated when the load operation is com-piled in cloud services rather than when the record is inserted into the table (i.e. when the transaction for the load operation is committed).
C. LOAD_TIME column values returned by COPY_HISTORY view (Account Usage) gives the same time as returned by CURRENT_TIMESTAMP.
D. CURRENT_TIMESTAMP values might be different due to query gets executed in warehouse located in different region.

Answer: B

Explanation:
Explanation
The reason timestamps are earlier than the LOAD_TIME column values which is returned by COPY_HISTORY view (Account Usage) is that CURRENT_TIMESTAMP is evaluated when the load operation is compiled in cloud services rather than when the record is inserted into the table (i.e. when the transaction for the load operation is committed).

NEW QUESTION # 121
......

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