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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q195-Q200):

NEW QUESTION # 195
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.
B. Use Athena partition projection based on the S3 bucket prefix.
C. Transform the data that is in the S3 bucket to Apache Parquet format.
D. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
E. Create an AWS Glue partition index. Enable partition filtering.

Answer: B,E

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

NEW QUESTION # 196
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.
Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM.
A business intelligence team queries the tables between 10 AM and 8 PM every day.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.
B. Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day.Overwrite the previous day's full-load copy every day.
C. Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.
D. Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

Answer: A

NEW QUESTION # 197
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats.
The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. Amazon S3 Transfer Acceleration
B. AWS Glue
C. AWS Direct Connect
D. AWS DataSync

Answer: D

Explanation:
AWS DataSync is a managed data transfer service that simplifies and accelerates moving large amounts of data online between on-premises storage and Amazon S3, EFS, or FSx for Windows File Server. DataSync is optimized for efficient, incremental, and reliable transfers of large datasets, making it suitable for transferring 5 TB of data with daily updates.

NEW QUESTION # 198
A company built a data lake and a data warehouse on AWS. The company wants to implement a data catalog to enhance the current data storage solutions. The company wants to have the capability to add business metadata and glossary information to the data catalog for every asset.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon DataZone. Create the business glossaries. Create metadata forms. Use the Amazon DataZone data portal to access the metadata.
B. Use an Apache Hive metastore. Create a user table for the business glossary. Use the ALTER TABLE command to change table properties to add business metadata. Create a web application to access the metadata.
C. Use Amazon OpenSearch Service. Create an index for the business glossary. Create a second index for the business metadata. Use the OpenSearch Service dashboard to access the metadata.
D. Use AWS Glue Catalog. Create a user table for the business glossary. Use the AWS Glue API to change table properties to add business metadata. Create a web application to access the metadata.

Answer: A

Explanation:
Amazon DataZone provides a fully managed data catalog and governance service with built-in support for business glossaries, custom metadata forms, and a user-friendly data portal. You can onboard your existing data assets, define glossaries, and capture business metadata without building or maintaining custom tables, APIs, or applications, minimizing operational overhead.

NEW QUESTION # 199
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

A. Increase the maximum number of task nodes for EMR managed scaling to 10.
B. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.
C. Reduce the scaling cooldown period for the provisioned EMR cluster.
D. Switch the task node type from general purpose Re instances to compute optimized EC2 instances.

Answer: D

Explanation:
Since the ETL job reaches maximum CPU usage but not memory usage, switching from general- purpose instances to compute-optimized instances (such as C5 or C6g instances) can provide better performance per dollar for CPU-bound workloads.

NEW QUESTION # 200
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