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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion	25%	- Handle file formats, compression, encoding, and schema detection - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Ingest from various sources: cloud storage, on-premises, APIs, data lakes
Topic 2: Performance Optimization & Scalability	15%	- Monitor and tune workloads and resource usage - Optimize warehouses, clustering, partitioning, and query performance - Design scalable compute and storage solutions
Topic 3: Data Pipeline Orchestration & Automation	20%	- Error handling, retry logic, and dependency management - Integrate with external orchestration tools - Build, schedule, monitor, and manage workflows
Topic 4: Data Transformation & Processing	30%	- Use streams, tasks, dynamic tables for pipelines and CDC - Work with semi-structured and unstructured data - Design and implement transformations: SQL, stored procedures, user-defined functions - Implement data quality, validation, and schema evolution
Topic 5: Data Governance, Security & Sharing	10%	- Share data securely across accounts and platforms - Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Can Masking policies be applied to virtual columns?

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 31
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

A. Deploy are Amazon Elastic Block Store (Amazon EBS) volume for the job to use.
B. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.
C. Use the AWS Glue Spark shuffle manager.
D. Convert the sort-merge join in the job to be a broadcast join.

Answer: C

Explanation:
The error "No space left on device" during a sort-merge join typically results from insufficient disk space during the shuffle phase of a Spark job. Using the AWS Glue Spark shuffle manager (optimized shuffle manager) helps reduce disk usage and improves shuffle performance by optimizing how intermediate data is written and read. This directly addresses the issue without requiring job logic changes or manual resource management.

NEW QUESTION # 32
A media company wants to build a real-time analytics pipeline to process customer activity events across the company's website and mobile app. The company wants to build a solution to ingest millions of events with minimum latency. The solution must be scalable and durable enough so that no data is lost. Which solution will meet these requirements in the MOST cost-effective way?

A. Schedule an AWS Glue job to fetch user interaction logs every 10 minutes from Amazon S3.
Configure the AWS Glue job to transform and store the data in Amazon Redshift for analytics.
B. Deploy an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. Use self- managed consumers to process and distribute data in real-time. Integrate with Amazon Redshift for enhanced analytics.
C. Set up an Amazon Kinesis Data Streams pipeline to ingest data, process the data by using AWS Lambda functions, and store the results in Amazon Redshift for analytics.
D. Configure Amazon S3 Event Notifications to invoke an AWS Lambda function to process every new interaction log file. Store the result in Amazon Redshift for analytics.

Answer: C

Explanation:
Amazon Kinesis Data Streams is built for durable, scalable, real-time ingestion of streaming events and can process data with very low latency at high volume. AWS documents that Kinesis Data Streams supports real-time stream processing and integrates directly with Amazon Redshift for streaming ingestion, which fits a pipeline handling millions of events without waiting for batch intervals. Using AWS Lambda for stream processing keeps the solution managed and cost- effective, while Amazon MSK would introduce higher cluster cost and operational complexity for this requirement.
Reference:
https://docs.aws.amazon.com/streams/latest/dev/introduction.html

NEW QUESTION # 33
Which query will show a list of the 20 most recent executions of a specified task kttask, that have been scheduled within the last hour that have ended or are stillrunning's.

Answer: C

NEW QUESTION # 34
A company uses Amazon S3 and AWS Glue Data Catalog to manage a data lake that contains contact information for customers. The company uses PySpark and AWS Glue jobs with a DynamicFrame to run a workflow that processes data within the data lake.
A data engineer notices that the workflow is generating errors as a result of how customer postal codes are stored in the data lake. Some postal codes include unnecessary numbers or invalid characters.
The data engineer needs a solution to address the errors and correct the postal codes in the data lake.

A. Use AWS Glue workflow properties to allow job state sharing. Configure the AWS Glue jobs to read values from the postal code column by using the properties from a previously successful run of the jobs.
B. Configure the column.push_down_predicate setting and the catalogPartitionPredicate settings for the postal code column in the DynamicFrame.
C. Set the DynamicFrame additional_options parameter `useS3ListImplementation' to True.
D. Create a schema definition for PySpark that matches the format the processing workflow requires for postal codes. Pass the schema to the DynamicFrame during processing.

Answer: D

Explanation:
By defining and enforcing a strict schema in PySpark that specifies the exact format you expect for postal codes (for example, a string of five digits), Spark will automatically flag or coerce any values that don't match. Passing that schema into your Glue DynamicFrame ensures that malformed postal codes are identified and can be corrected or filtered out as part of your ETL logic, eliminating the downstream errors.

NEW QUESTION # 35
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