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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	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 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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q269-Q274):

NEW QUESTION # 269
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.
Which solution will meet these requirements with the LEAST effort?

A. Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.
B. Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog.
Use data stewards to inspect the data and update the data catalog with the data format.
C. Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.
D. Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: C

Explanation:
AWS Glue crawlers can automatically scan data in Amazon S3 buckets and Amazon RDS databases to build a data catalog. Glue crawlers also have classifiers that can automatically detect the format of the data (such as CSV, JSON, Parquet, etc.) and store this information as metadata in the Data Catalog. This solution automates the process of cataloging and format recognition, meeting the requirement with the least effort.
The "Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format." option requires manual inspection and updating of the data catalog by data stewards, which adds significant effort and is unnecessary since Glue crawlers can automatically detect the format.
Amazon Macie is primarily used for identifying sensitive data (e.g., PII), not for building a comprehensive data catalog or identifying data formats. It doesn't meet the requirement of cataloging storage format metadata.
Writing custom scripts to scan and classify data based on format is much more labor-intensive compared to using an automated Glue crawler, which handles this task with much less effort.

NEW QUESTION # 270
A company has multiple applications that use datasets that are stored in an Amazon S3 bucket.
The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.
B. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.
C. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.
D. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset.
Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

Answer: B

Explanation:
Amazon S3 Object Lambda allows you to add your own code to S3 GET requests to modify and process data as it is returned to an application. For example, you could use an S3 Object Lambda to dynamically redact personally identifiable information (PII) from data retrieved from S3. This would allow you to control access to sensitive information based on the needs of different applications, without having to create and manage multiple copies of your data.

NEW QUESTION # 271
A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.
B. Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.
C. Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.
D. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.

Answer: B

Explanation:
By using a serverless Glue streaming job to continuously pull your MSK records, transform them as needed, and land them in Parquet (or another columnar) files in S3, you:
1. Optimize storage costs ?your data sits in S3, where you pay pennies per GB-month and can tier it further with lifecycle rules.
2. Get low-latency access ?Redshift Spectrum lets you query S3-backed tables with millisecond planning time, so freshly landed data becomes queryable almost immediately.
3. Minimize ops overhead ?you don't have to stand up or manage any EC2-based brokers, Lambda polling loops, or custom connector infrastructure.
Glue's managed streaming runtime handles checkpointing, autoscaling, and fault tolerance for you.
Once the data lands in S3, you simply define an external schema in Redshift that points at the Glue Data Catalog database where your streaming job writes tables. Analysts can then query the
"live" dataset via Spectrum as if it were inside Redshift, meeting both your performance and cost goals with minimal operational effort.

NEW QUESTION # 272
As a skilled data engineer, you have been tasked with managing the Spark ETL jobs that run on the EMR and store incremental data in S3. However, you have noticed that some jobs are failing, and you are eager to take proactive steps to troubleshoot the issue. What would be the primary course of action to resolve this problem and ensure the smooth operation of the ETL process?

A. Review the Spark job logs for errors or warnings.
B. Increase the number of EC2 instances in the cluster.
C. Check the available disk space on the HDFS cluster.
D. Restart the EMR cluster.

Answer: A

Explanation:
Reviewing Spark job logs is the first step to identify errors, warnings, or slow stages that indicate the root cause of the performance issue.

NEW QUESTION # 273
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB. Amazon RDS. Amazon Redshift. and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time. Which solution will meet these requirements?

A. Use Amazon Athena Federated Query to perform one-time joins and analysis across DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3.
B. Set up an AWS Glue ETL pipeline to extract, transform, and centralize data in Amazon S3. Use Amazon Athena to run analytical queries.
C. Capture data from DynamoDB by using DynamoDB Streams. Migrate data from Amazon RDS by using AWS DMS. Export Amazon Redshift data. Store all data in Amazon S3. Use Redshift Spectrum to run queries.
D. Deploy an Amazon EMR cluster powered by Apache Spark to ingest, process, and merge datasets from multiple sources. Run analytical workloads on the merged data.

Answer: A

Explanation:
Amazon Athena Federated Query can run SQL across data in Amazon S3 and external sources by using data source connectors, and AWS provides connectors for Amazon DynamoDB, Amazon RDS, and Amazon Redshift. That lets the company perform a one-time cross-source join and analysis without first centralizing or copying the data, which minimizes unnecessary data movement and setup effort.
Reference:
https://docs.aws.amazon.com/athena/latest/ug/federated-queries.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-redshift.html
https://docs.aws.amazon.com/athena/latest/ug/connectors-dynamodb.html

NEW QUESTION # 274
......

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