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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q45-Q50):

NEW QUESTION # 45
A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket.
The process needs to rename a column, remove specific columns, ignore the second row of each file, create a new column based on the values of the first row of the data, and filter the results by a numeric value of a column.
Which solution will meet these requirements with the LEAST development effort?

A. Use AWS Glue Python jobs to read and transform the CSV files.
B. Use an AWS Glue custom crawler to read and transform the CSV files.
C. Use AWS Glue DataBrew recipes to read and transform the CSV files.
D. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.

Answer: C

Explanation:
AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks.
While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew.
AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns.
Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.

NEW QUESTION # 46
A data engineer must orchestrate a series of Amazon Athena queries that will run every day.
Each query can run for more than 15 minutes.
Which combination of steps will meet these requirements MOST cost-effectively? (Choose two.)

A. Use an AWS Glue Python shell job and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
B. Create an AWS Step Functions workflow and add two states. Add the first state before the Lambda function. Configure the second state as a Wait state to periodically check whether the Athena query has finished using the Athena Boto3 get_query_execution API call. Configure the workflow to invoke the next query when the current query has finished running.
C. Use an AWS Lambda function and the Athena Boto3 client start_query_execution API call to invoke the Athena queries programmatically.
D. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the Athena queries in AWS Batch.
E. Use an AWS Glue Python shell script to run a sleep timer that checks every 5 minutes to determine whether the current Athena query has finished running successfully. Configure the Python shell script to invoke the next query when the current query has finished running.

Answer: B,C

NEW QUESTION # 47
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.
B. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.
C. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.
D. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.

Answer: B

Explanation:
AWS CloudTrail provides detailed logging of AWS API calls, including object-level events for Amazon S3. You can configure CloudTrail to track data events for the S3 bucket, which will log each access to the objects in the bucket, including the username of the IAM entity (user or role) that accessed the data. By forwarding these CloudTrail events to Amazon CloudWatch, you can set up alarms to trigger notifications when policy violations are detected, and include the violating user's identity.
AWS Config is primarily designed for monitoring resource configurations, not for tracking real- time access events like data access violations. It does not directly provide information about which user accessed the S3 objects.
CloudWatch metrics can monitor S3 storage or request metrics, but it does not provide detailed logging of object-level access, including the username of the violator. You need CloudTrail to get detailed event information.
While S3 server access logs can track access to objects, they lack real-time processing and do not provide the same level of detail as CloudTrail.
Additionally, processing access logs with CloudWatch metric filters adds more complexity compared to using CloudTrail, which is designed for this use case.

NEW QUESTION # 48
A company stores customer data that contains personally identifiable information (PII) in an Amazon Redshift cluster. The company's marketing, claims, and analytics teams need to be able to access the customer data.
The marketing team should have access to obfuscated claim information but should have full access to customer contact information. The claims team should have access to customer information for each claim that the team processes. The analytics team should have access only to obfuscated PII data.
Which solution will enforce these data access requirements with the LEAST administrative overhead?

A. Create a separate Amazon Redshift database role for each team. Define masking policies that apply for each team separately. Attach appropriate masking policies to each team role.
B. Move the customer data to an Amazon S3 bucket. Use AWS Lake Formation to create a data lake. Use fine-grained security capabilities to grant each team appropriate permissions to access the data.
C. Create a separate Redshift cluster for each team. Load only the required data for each team.
Restrict access to clusters based on the teams.
D. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.

Answer: A

NEW QUESTION # 49
Select the Correct statements with regard to using Federated authentication/SSO?

A. Snowflake supports SSO with Private Connectivity to the Snowflake Service for Snow-flake accounts on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform.
B. Snowflake supports using SSO with organizations, and you can use the corresponding URL in the SAML2 security integration.
C. Snowflake supports multiple audience values (i.e. Audience or Audience Restriction Fields) in the SAML 2.0 assertion from the identity provider to Snowflake.
D. Snowflake supports using MFA in conjunction with SSO to provide additional levels of security.

Answer: A,B,C,D

NEW QUESTION # 50
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