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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	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 3	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 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	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 (Q181-Q186):

NEW QUESTION # 181
Which one is not the Core benefits of micro-partitioning

A. Columns are also compressed individually within micro-partitions.
B. Columns are stored independently within micro-partitions, often referred to as colum-nar storage.
C. Snowflake micro-partitions are derived automatically they do not need to be explicitly defined up-front or maintained by users.
D. Enables extremely efficient DML and fine-grained pruning for faster queries.
E. Micro-partitions can overlap in their range of values, helps data skewing.

Answer: E

Explanation:
Explanation
The benefits of Snowflake's approach to partitioning table data include:
In contrast to traditional static partitioning, Snowflake micro-partitions are derived automatically; they don't need to be explicitly defined up-front or maintained by users.
As the name suggests, micro-partitions are small in size (50 to 500 MB, before compression), which enables extremely efficient DML and fine-grained pruning for faster queries.
Micro-partitions can overlap in their range of values, which, combined with their uniformly small size, helps prevent skew.
Columns are stored independently within micro-partitions, often referred to as columnar storage. This enables efficient scanning of individual columns; only the columns referenced by a query are scanned.
Columns are also compressed individually within micro-partitions. Snowflake automatically de-termines the most efficient compression algorithm for the columns in each micro-partition.

NEW QUESTION # 182
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse.
The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

A. Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
B. Use the SUPER data type to store the data in the Amazon Redshift table.
C. Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.
D. Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

Answer: B

Explanation:
The SUPER data type in Amazon Redshift allows you to natively store semi-structured data such as JSON. By using the SUPER data type, you can store JSON data in a Redshift table without needing to flatten or transform it beforehand. This approach requires the least effort because it allows you to directly load the JSON data and query it using Redshift's JSONPath and PARTITION BY capabilities without additional ETL processing.

NEW QUESTION # 183
A data engineer must use AWS services to ingest a dataset into an Amazon S3 data lake. The data engineer profiles the dataset and discovers that the dataset contains personally identifiable information (PII). The data engineer must implement a solution to profile the dataset and obfuscate the PII.
Which solution will meet this requirement with the LEAST operational effort?

A. Use the Detect PII transform in AWS Glue Studio to identify the PII. Create a rule in AWS Glue Data Quality to obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
B. Use the Detect PII transform in AWS Glue Studio to identify the PII. Obfuscate the PII. Use an AWS Step Functions state machine to orchestrate a data pipeline to ingest the data into the S3 data lake.
C. Ingest the dataset into Amazon DynamoDB. Create an AWS Lambda function to identify and obfuscate the PII in the DynamoDB table and to transform the data. Use the same Lambda function to ingest the data into the S3 data lake.
D. Use an Amazon Kinesis Data Firehose delivery stream to process the dataset. Create an AWS Lambda transform function to identify the PII. Use an AWS SDK to obfuscate the PII. Set the S3 data lake as the target for the delivery stream.

Answer: A

NEW QUESTION # 184
What are Invalid rules applicable when using stored procedure contains transaction?

A. If a transaction is started inside a stored procedure and is still active when the stored procedure finishes, then an error occurs, and the transaction is rolled back.
B. All Rules are Applicable.
C. You cannot start a transaction inside the stored procedure, then complete the transac-tion after returning from the procedure.
D. A transaction inside a stored procedure can include a call to another stored procedure that contains a transaction.
E. A transaction can be inside a stored procedure, or a stored procedure can be inside a transaction.
F. You cannot start a transaction before calling the stored procedure, then complete the transaction inside the stored procedure.

Answer: B

NEW QUESTION # 185
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 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.
B. 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.
C. 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.
D. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.

Answer: C

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 # 186
......

If you want to success in your career as a Snowflake Certified Professional, you must think outside the box. It would be beneficial if you considered adding SnowPro Advanced: Data Engineer Certification Exam to your resume. To get this certification, you must pass the DEA-C01 exam conducted by Snowflake. Passing the SnowPro Advanced: Data Engineer Certification Exam exam will help you advance your career. It is not an easy task to pass the SnowPro Advanced: Data Engineer Certification Exam certification exam on the first attempt, but now CramPDF is here to help. To assist you with remote study, CramPDF provides Snowflake DEA-C01 Exam Questions to make your test preparation complete. The Snowflake DEA-C01 exam questions simulate the actual exam pattern, allowing you to pass the SnowPro Advanced: Data Engineer Certification Exam certification exam the first time.

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