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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
How can the following relational data be transformed into semi-structured data using the LEAST amount of operational overhead?

A. Use the TO_VARIANT function to convert each of the relational columns to VARIANT.
B. Use the OBJECT_CONSTRUCT function to return a Snowflake object
C. Use the to_json function
D. Use the PAESE_JSON function to produce a variant value

Answer: B

Explanation:
Explanation
This option is the best way to transform relational data into semi-structured data using the least amount of operational overhead. The OBJECT_CONSTRUCT function takes a variable number of key-value pairs as arguments and returns a Snowflake object, which is a variant type that can store JSON data. The function can be used to convert each row of relational data into a JSON object with the column names as keys and the column values as values.

NEW QUESTION # 86
A data engineer is writing a query to join two tables in Amazon Athena. The data engineer needs to choose the correct join order for the tables to optimize query performance. Which solution will meet these requirements?

A. Use AWS Glue to pre-process the tables before performing the join.
B. Use table statistics to automatically determine the join order.
C. Specify the larger table on the left side of the join and the smaller table on the right side of the join.
D. Specify the smaller table on the left side of the join and the larger table on the right side of the join.

Answer: D

Explanation:
Amazon Athena (based on Presto/Trino) performs joins more efficiently when the smaller table is placed on the left side because it is used as the build side of the join and can be loaded into memory. This minimizes data shuffling and improves performance when joining with a larger table on the right side.

NEW QUESTION # 87
A company needs to send customer call data from its on-premises PostgreSQL database to AWS to generate near real-time insights. The solution must capture and load updates from operational data stores that run in the PostgreSQL database. The data changes continuously.
A data engineer configures an AWS Database Migration Service (AWS DMS) ongoing replication task. The task reads changes in near real time from the PostgreSQL source database transaction logs for each table. The task then sends the data to an Amazon Redshift cluster for processing.
The data engineer discovers latency issues during the change data capture (CDC) of the task.
The data engineer thinks that the PostgreSQL source database is causing the high latency.
Which solution will confirm that the PostgreSQL database is the source of the high latency?

A. Enable Amazon CloudWatch Logs for the DMS endpoint of the source database. Check for error messages.
B. Verify that logical replication of the source database is configured in the postgresql.conf configuration file.
C. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCIncomingChanges metric to identify delays in the CDC from the source database.
D. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCLatencySource metric to identify delays in the CDC from the source database.

Answer: D

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Troubleshooting_Latency.html A high CDCLatencySource metric indicates that the process of capturing changes from the source is delayed.

NEW QUESTION # 88
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. Filter
B. Flatten
C. Join
D. Aggregate
E. UNION ALL

Answer: D

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 # 89
Company DEF has a strict security policy that mandates that all data at rest in Amazon S3 must be encrypted. They want to ensure that the encryption keys are managed by AWS, but they also want the flexibility to change the encryption keys when required.
Which of the following encryption methods best meets Company DEF's requirements?

A. Server-Side Encryption with Customer-Provided Keys (SSE-C).
B. Client-Side Encryption with a client-side master key.
C. Server-Side Encryption with AWS Key Management Service (SSE-KMS).
D. Server-Side Encryption with Amazon S3 Managed Keys (SSE-S3).

Answer: C

NEW QUESTION # 90
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