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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	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 3	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 4	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 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 (Q251-Q256):

NEW QUESTION # 251
A popular e-commerce platform experiences heavy traffic during peak hours, resulting in high latency for frequently accessed product listings. To improve performance and response times, the platform decides to implement caching for the most frequently queried product data. They aim to store the results of these queries in a highly available, low-latency cache to reduce database load and improve overall user experience.
Which AWS service would be most suitable for implementing the caching for the most frequent queries and storing their results as a cache?

A. Implement S3 Gateway endpoint and store results temporarily in S3.
B. Use Amazon MemoryDB for Redis.
C. Use Amazon Redshift.
D. Use Amazon DynamoDB with DAX.

Answer: B

Explanation:
Amazon MemoryDB for Redis is a fully managed Redis-compatible, in-memory database service that provides enhanced durability, replication, and performance. It is optimized for caching frequently accessed data and provides sub-millisecond latency, making it suitable for improving response times and reducing database load in high-traffic scenarios such as an e-commerce platform.
References:
https://aws.amazon.com/redshift/
https://aws.amazon.com/dynamodb/dax/
https://aws.amazon.com/s3/
https://aws.amazon.com/memorydb/

NEW QUESTION # 252
As Data Engineer, you have been asked to access data held in AWS Glacier Deep Archive storage class for Historical Data Analysis, which one is the correct statement to recommend?

A. We can simply access AWS Glacier Deep Archive storage External Stage data using PUT command.
B. You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved.
C. Data can be accessed from External stage using AWS Private link in this case.
D. Loading data from AWS cloud storage services is supported regardless of the cloud platform that hosts your Snowflake account.
E. Upload (i.e. stage) files to your cloud storage account using the tools provided by the cloud storage service.

Answer: B

Explanation:
Explanation
External stage
References data files stored in a location outside of Snowflake. Currently, the following cloud stor-age services are supported:
Amazon S3 buckets
Google Cloud Storage buckets
Microsoft Azure containers
The storage location can be either private/protected or public.
You cannot access data held in archival cloud storage classes that requires restoration before it can be retrieved. These archival storage classes include, for example, the Amazon S3 Glacier Flexible Retrieval or Glacier Deep Archive storage class, or Microsoft Azure Archive Storage.

NEW QUESTION # 253
As Data Engineer, you have requirement to Load set of New Product Files containing Product rele-vant information into the Snowflake internal tables, Later you analyzed that some of the Source files are already loaded in one of the historical batch & for that you have prechecked Metadata col-umn LAST_MODIFIED date for a staged data file & found out that LAST_MODIFIED date is older than 64 days for few files and the initial set of data was loaded into the table more than 64 days earlier, Which one is the best approach to Load Source data files with expired load metadata along with set of files whose metadata might be available to avoid data duplication?

A. The COPY command cannot definitively determine whether a file has been loaded al-ready if the LAST_MODIFIED date is older than 64 days and the initial set of data was loaded into the table more than 64 days earlier (and if the file was loaded into the table, that also occurred more than 64 days earlier). In this case, to prevent accidental reload, the command skips the product files by default.
B. To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true.
C. Since the initial set of data for the table (i.e. the first batch after the table was created) was loaded, we can simply use the COPY INTO command to load all the product files with the known load status irrespective of their column LAST_MODIFIED date values.
D. Set the FORCE option to load all files, ignoring load metadata if it exists.

Answer: B

Explanation:
Explanation
To load files whose metadata has expired, set the LOAD_UNCERTAIN_FILES copy option to true. The copy option references load metadata, if available, to avoid data duplication, but also at-tempts to load files with expired load metadata.
Alternatively, set the FORCE option to load all files, ignoring load metadata if it exists. Note that this option reloads files, potentially duplicating data in a table.
Please refer the Example as mentioned in the link below:
https://docs.snowflake.com/en/user-guide/data-load-considerations-load.html#loading-older-files

NEW QUESTION # 254
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules.
The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

A. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.
B. Develop custom Python scripts to detect the custom PII categories. Call the scripts from DataBrew.
C. Manually review the data for custom PII categories.
D. Implement regex patterns to extract PII information from fields during extract transform, and load (ETL) operations into the data lake.

Answer: A

Explanation:
AWS Glue DataBrew allows users to create custom data quality rules, including rules for detecting non-standard PII categories. By implementing custom rules, the data engineer can automate the detection of PII (including the custom categories) across multiple datasets without needing to manually review or develop custom scripts. This provides a solution with the least operational overhead, as it leverages the existing DataBrew framework and automation capabilities.
Manually reviewing the data is highly labor-intensive, error-prone, and not scalable, especially across multiple datasets.
While possible, writing custom Python scripts adds operational overhead, as it requires developing, maintaining, and integrating scripts with DataBrew.
This increases complexity compared to implementing custom rules directly within DataBrew.
While regex can be useful, incorporating it directly into ETL processes adds operational complexity and doesn't leverage the capabilities of DataBrew, which is already designed for data cleaning and quality validation.

NEW QUESTION # 255
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?

A. Use AWS CLI to gather the information.
B. Use AWS usage reports for Amazon S3 to gather the information.
C. Use Amazon S3 Inventory configurations reports to gather the information.
D. Use the Amazon S3 Storage Lens dashboard to gather the information.

Answer: D

Explanation:
Amazon S3 Storage Lens provides a comprehensive view of S3 storage usage and activity. It includes metrics such as the number of incomplete multipart uploads and the number of non- current object versions, making it an ideal tool for gathering insights into storage inefficiencies like outdated versions and incomplete uploads. This solution requires minimal operational effort as it is built into S3 and provides a user-friendly dashboard with detailed insights.
Using the AWS CLI to manually list and check multipart uploads and object versions would require scripting and regular execution, leading to higher operational overhead.
S3 Inventory provides reports on the objects in your bucket, but it does not directly provide information on incomplete multipart uploads or outdated versions. You would need to process the data further to gather the specific insights.
AWS usage reports are more focused on billing and usage rather than detailed storage optimization metrics like incomplete multipart uploads and outdated object versions.

NEW QUESTION # 256
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