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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
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
Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data - Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage
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
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. Batch loading strategies 2. Continuous ingestion concepts
Performance Optimization	- Warehouse optimization 1. Concurrency management 2. Scaling strategies - Query performance tuning 1. Caching mechanisms 2. Clustering and micro-partitions
Security, Governance, and Data Sharing	- Access control 1. Object-level permissions 2. Role-based access control (RBAC) - Data governance 1. Secure data sharing concepts 2. Data masking policies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A multinational corporation operating across various sectors has deployed its data infrastructure on AWS, utilizing Amazon S3 for object storage, Amazon Redshift for data warehousing, and Amazon RDS for relational databases. The data analyst team, responsible for extracting insights from these diverse data sources, seeks a solution to query and analyze data across all platforms efficiently. They aim for a unified querying platform that seamlessly integrates with different data stores, allowing for holistic analysis without the overhead of data movement or complex transformations.
Which of the following approaches will meet the requirements of the data analyst team with the least operational effort and most cost-friendly? (Select TWO)

A. Utilize AWS Glue Data Catalog to create a centralized metadata repository.
B. Use Amazon Redshift Spectrum to query data stored in Amazon S3 buckets directly from within Amazon Redshift clusters, enabling seamless querying of structured and unstructured data.
C. Use Amazon Athena Federated queries to directly query data stored in respective data sources using standard SQL, providing a serverless solution for querying data without the need for data movement.
D. Leverage Amazon EMR with Apache Hive for querying and processing data stored in various AWS data sources, enabling distributed querying and analysis across multiple data stores.
E. Utilize Amazon Aurora with Federated Query to query data from multiple Amazon RDS databases and other data sources using SQL, providing a unified view of the data for analysis.

Answer: A,C

Explanation:
AWS Glue Data Catalog can be used to create a centralized metadata repository, providing a unified view of the data across different data sources. This approach requires minimal operational effort as it involves creating and managing metadata, and it is cost-friendly compared to other options. Amazon Athena allows federated queries to directly query data stored in respective data sources using standard SQL. It provides a serverless solution for querying data without the need for data movement, resulting in minimal operational effort and cost.
References:
https://aws.amazon.com/glue/features/catalog/
https://aws.amazon.com/athena/
https://aws.amazon.com/redshift/spectrum/
https://aws.amazon.com/blogs/aws/new-query-your-data-lake-with-amazon-aurora-federated-query/
https://aws.amazon.com/emr/

NEW QUESTION # 18
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?

A. DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0
B. DatasetMatch "reference" "city->ref_city, state->ref_state" = 100
C. Referentialintegrity "city,state" "reference.{ref_city,ref_state}" = 100
D. Referentiallntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

Answer: D

Explanation:
The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those columns without extra overhead.

NEW QUESTION # 19
A data engineer must maintain and monitor a data pipeline on AWS that processes streaming data from Internet of Things (IoT) devices. The pipeline uses Amazon Kinesis Data Streams to ingest data and Amazon Data Firehose to deliver data to an Amazon S3 bucket. The data engineer needs to monitor the health of the pipeline. Which solution will meet these requirements with the LEAST operational effort?

A. Use Amazon Managed Service for Apache Flink to perform near real-time anomaly detection on the streaming data and to invoke alerts if unusual patterns are detected.
B. Use an AWS Lambda function to run daily checks on the status of the Kinesis Data Streams and Firehose. Configure the Lambda function to use Amazon Simple Notification Service (Amazon SNS) to send notifications.
C. Use Amazon CloudWatch Logs to manually review logs that are generated by Kinesis Data Streams and Firehose.
D. Configure Amazon CloudWatch alarms to monitor key metrics such as IncomingBytes, OutgoingBytes, and DeliveryToS3.Success for Kinesis Data Streams and Firehose

Answer: D

Explanation:
CloudWatch metrics for Kinesis Data Streams and Firehose are available automatically and provide direct visibility into ingestion, throughput, and delivery success. Configuring CloudWatch alarms on key metrics (such as stream input/output and Firehose delivery success) enables proactive, automated health monitoring and alerting with minimal setup and no custom code or additional services to operate.

NEW QUESTION # 20
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

A. git rebase master
B. git pull master
C. git fetch -b master
D. git diff branchB master
git commit -m

Answer: A

Explanation:
Rebasing
In Git, there are two main ways to integrate changes from one branch into another: the merge and the rebase. In this section you'll learn what rebasing is, how to do it, why it's a pretty amazing tool, and in what cases you won't want to use it.
The Basic Rebase
If you go back to an earlier example from Basic Merging, you can see that you diverged your work and made commits on two different branches.

NEW QUESTION # 21
A data engineer maintains custom Python scripts that perform a data formatting process that many AWS Lambda functions use. When the data engineer needs to modify the Python scripts, the data engineer must manually update all the Lambda functions.
The data engineer requires a less manual way to update the Lambda functions.
Which solution will meet this requirement?

A. Store a pointer to the custom Python scripts in environment variables in a shared Amazon S3 bucket.
B. Package the custom Python scripts into Lambda layers. Apply the Lambda layers to the Lambda functions.
C. Assign the same alias to each Lambda function. Call reach Lambda function by specifying the function's alias.
D. Store a pointer to the custom Python scripts in the execution context object in a shared Amazon S3 bucket.

Answer: B

Explanation:
After you create a layer, you can apply it to any number of functions in your account. Without layers, you need to include the same dependencies in each individual deployment package.
https://docs.aws.amazon.com/lambda/latest/dg/chapter-layers.html

NEW QUESTION # 22
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