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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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.
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	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q202-Q207):

NEW QUESTION # 202
An ecommerce company collects daily customer transaction logs in CSV format and stores the logs in Amazon S3. The company uses Amazon Athena to scan a subset of attributes from the logs on the same day the company receives each log.
Query times are increasing because of increasing transaction volume. The company wants to improve query performance.
Which solution will meet these requirements with the SHORTEST query times?

A. Convert the CSV logs into multiple ORC files for better parallelism in Athena. Partition by date in Amazon S3. Use columnar pushdown filters.
B. Convert the CSV logs to JSON. Partition by date in Amazon S3. Use Athena with dynamic filtering to reduce data scans.
C. Convert the CSV logs to Avro. Partition by date in Amazon S3. Use Athena with projection-based partitioning.
D. Convert the CSV logs to a single Apache Parquet file for each day Partition the data by date in Amazon S3. Use Athena with predicate pushdown filters.

Answer: D

Explanation:
Partitioning by date limits each query to only the relevant day's data, and Parquet enables efficient columnar reads and predicate pushdown for scanning only the needed columns and row groups. Writing a single Parquet file per day minimizes file listing and small-file overhead, typically delivering the shortest Athena query times for same-day, subset-column analytics.

NEW QUESTION # 203
A company extracts approximately 1 TB of data every day from data sources such as SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. Some of the data sources have undefined data schemas or data schemas that change.
A data engineer must implement a solution that can detect the schema for these data sources.
The solution must extract, transform, and load the data to an Amazon S3 bucket. The company has a service level agreement (SLA) to load the data into the S3 bucket within 15 minutes of data creation.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create a PySpark program in AWS Lambda to extract, transform, and load the data into the S3 bucket.
B. Use Amazon EMR to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
C. Use AWS Glue to detect the schema and to extract, transform, and load the data into the S3 bucket. Create a pipeline in Apache Spark.
D. Create a stored procedure in Amazon Redshift to detect the schema and to extract, transform, and load the data into a Redshift Spectrum table. Access the table from Amazon S3.

Answer: C

NEW QUESTION # 204
A media company wants to improve a system that recommends media content to customers based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
B. Use API calls to access and integrate third-party datasets from AWS DataSync.
C. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from a Git repository.
D. Use API calls to access and integrate third-party datasets from AWS Data Exchange.

Answer: D

Explanation:
AWS Data Exchange is designed to quickly subscribe to and consume third-party datasets through a managed service interface and APIs, minimizing the integration effort and operational overhead compared to building custom ingestion from other sources.

NEW QUESTION # 205
A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and load data into Amazon Redshift.
The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to orchestrate the jobs.
Which service will meet these requirements?

A. AWS Step Functions
B. AWS Glue
C. Amazon EventBridge
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: D

Explanation:
Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run Apache Airflow at scale. Airflow is a popular tool for orchestrating complex workflows, including ETL jobs, and can handle various task types such as running Apache Spark jobs on Amazon EMR, making API calls to external systems like Salesforce, and loading data into Amazon Redshift. Airflow allows you to define workflows using Python and offers built-in mechanisms for handling failures, retries, and retries automatically, making it a great fit for this use case.
While AWS Step Functions can orchestrate workflows and handle retries and errors, it is less suited for Python-based orchestration compared to Airflow. Also, Step Functions is more suited for AWS services and does not offer built-in support for external systems like Salesforce, which would require custom integrations.
AWS Glue is a serverless ETL service, but it primarily focuses on data transformation tasks. Glue does not handle orchestration of complex workflows involving non-Glue jobs, such as calling Salesforce APIs or running Spark jobs on EMR, as effectively as Airflow.
EventBridge is an event-driven service, not an orchestration service. It is designed for event- based workflows but does not offer the workflow orchestration capabilities needed for handling tasks like retries, error handling, and running complex workflows across different systems.

NEW QUESTION # 206
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. 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.
B. 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.
C. Create views that include required fields for each of the data requirements. Grant the teams access only to the view that each team requires.
D. Create a separate Redshift cluster for each team. Load only the required data for each team.
Restrict access to clusters based on the teams.

Answer: B

NEW QUESTION # 207
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