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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Security, Governance, and Data Sharing	- Data governance 1. Data masking policies 2. Secure data sharing concepts - Access control 1. Role-based access control (RBAC) 2. Object-level permissions
Topic 2: Performance Optimization	- Warehouse optimization 1. Concurrency management 2. Scaling strategies - Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms
Topic 3: Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies
Topic 4: Data Loading and Integration	- Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows - Bulk data loading 1. COPY INTO command usage 2. File format handling
Topic 5: Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data - Semi-structured data processing 1. VARIANT data type usage 2. JSON, Avro, Parquet handling
Topic 6: Data Pipelines and Transformation	- Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks - ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q290-Q295):

NEW QUESTION # 290
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

A. Configure a scheduled Amazon EventBridge rule to invoke an AWS Lambda function to check for data that is older than 1 month. Configure the Lambda function to delete old data.
B. Configure a stream on the DynamoDB table to invoke an AWS Lambda function. Configure the Lambda function to delete data in the table that is older than 1 month.
C. Use the DynamoDB TTL feature to automatically expire data based on timestamps.
D. Use an AWS Lambda function to periodically scan the DynamoDB table for data that is older than
1 month. Configure the Lambda function to delete old data.

Answer: C

Explanation:
DynamoDB TTL (Time to Live) is a built-in feature that allows you to automatically delete expired items from your table based on a timestamp attribute.
Once TTL is enabled, DynamoDB automatically removes the items after they reach the expiration time, without disrupting any ongoing read or write operations. This solution provides the least ongoing maintenance because it's fully managed by DynamoDB and doesn't require manual interventions or additional Lambda functions.

NEW QUESTION # 291
A company is planning to deploy Amazon EMR in AWS to analyze vast amounts of data. Amazon EMR will be using the EMR file system. The project team is looking for options to deploy in the existing VPC created. Access to other AWS resources should be provided most securely.
How can the Amazon EMR cluster be deployed for this requirement?

A. Deploy Amazon EMR clusters in the private subnet of the VPC. Create an Interface endpoint to access Amazon S3 and DynamoDB.
B. Deploy Amazon EMR clusters in the public subnet of the VPC with access to Amazon S3 via the VPC Gateway endpoint. Access DynamoDB via an Internet gateway.
C. Deploy Amazon EMR clusters in a private subnet of the VPC with access to Amazon S3 and DynamoDB via NAT gateway.
D. Deploy Amazon EMR clusters in the public subnet of the VPC with access to Amazon S3 and DynamoDB via an Internet gateway.

Answer: A

Explanation:
Amazon EMR clusters can be deployed in the private subnet of the VPC. With the EMR file system, data is stored in Amazon S3 and it also requires access to DynamoDB. Access from private subnets to Amazon S3 and Amazon DynamoDB can be securely provided using the Interface endpoint.
Reference:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-clusters-in-a-vpc.html

NEW QUESTION # 292
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?

A. Apply partition filters in the queries.
B. Configure Spark to use in-memory caching for frequently accessed data.
C. Increase the frequency of AWS Glue crawler invocations to update the data catalog more often.
D. Organize the data that is in Amazon S3 by using a nested directory structure.

Answer: A

Explanation:
By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.

NEW QUESTION # 293
Which stages support external tables?

A. External stages only from any region, and any cloud provider
B. internal stages only from any Snowflake account in the organization
C. External stages only, only on the same region and cloud provider as the Snowflake account
D. Internal stages only; within a single Snowflake account

Answer: A

Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.

NEW QUESTION # 294
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size.
A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file.
Which solution will meet this requirement with the LEAST operational effort?

A. Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.
B. Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.
C. Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.
D. Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.

Answer: B

Explanation:
AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that allows data engineers and data analysts to clean and normalize data without writing code. Using DataBrew, a data engineer could create a recipe that includes the concatenation of the customer first and last names and then use the COUNT_DISTINCT function. This would not require complex code and could be performed through the DataBrew user interface, representing a lower operational effort.

NEW QUESTION # 295
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