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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	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.
Topic 3	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 4	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 5	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.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q141-Q146):

NEW QUESTION # 141
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real- time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Choose two.)

A. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
B. Use Amazon Redshift as a file storage system to archive old inventory management reports.
C. Use Amazon Redshift ML to generate inventory recommendations.
D. Use SQL to invoke a remote SageMaker endpoint for prediction.
E. Use Amazon Redshift ML to schedule regular data exports for offline model training.

Answer: C,D

Explanation:
Amazon Redshift ML integrates machine learning (ML) directly into the Redshift environment, allowing you to build and use ML models with SQL commands. By leveraging Redshift ML, the company can make real-time inventory recommendations based on historical and current data directly within Redshift.
Redshift can invoke external services, such as a SageMaker real-time endpoint, using SQL queries. This allows the company to send real-time data from Redshift to SageMaker and receive predictions (e.g., inventory forecasting) in real time, meeting the need for real-time predictions.

NEW QUESTION # 142
A company has a data lake in Amazon S3. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run.
A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?

A. Add a new custom transform to each Glue ETL job. Use the Great Expectations library to implement a ruleset that includes the data quality rules that need to be evaluated.
B. Add a new Evaluate Data Quality transform to each Glue ETL job. Use Data Quality Definition Language (DQDL) to implement a ruleset that includes the data quality rules that need to be evaluated.
C. Add a new transform that is defined by a SQL query to each Glue ETL job. Use the SQL query to implement a ruleset that includes the data quality rules that need to be evaluated.
D. Add a new custom transform to each Glue ETL job. Use the PyDeequ library to implement a ruleset that includes the data quality rules that need to be evaluated.

Answer: B

Explanation:
Evaluate Data Quality Transform in AWS Glue is a built-in feature that allows the application of Data Quality Definition Language (DQDL) to define and apply data quality rules directly within AWS Glue ETL jobs. This option offers the least implementation effort because it leverages a native AWS Glue feature, allowing data engineers to easily add and enforce data quality checks without needing custom code or external libraries.
While SQL can implement data quality rules, this approach requires manually writing and managing SQL queries within each job, leading to higher implementation effort compared to using built-in Glue functionality.
PyDeequ is an external library that requires custom coding and additional integration effort.
Although it is effective, it demands more implementation effort than using AWS Glue's built-in features.
Great Expectations is another external data quality library. While it is robust, it also involves custom coding and higher maintenance effort compared to AWS Glue's native data quality evaluation tools.

NEW QUESTION # 143
A data engineer needs to design a data pipeline that invokes an AWS Glue job. After the AWS Glue job finishes successfully, the pipeline needs to invoke three AWS Lambda functions. The pipeline must be serverless. The data engineer wants to see the entire pipeline lineage in a single interface.
Which solution will meet these requirements?

A. Build the pipeline in the AWS Glue job. Invoke the Lambda functions after the AWS Glue job runs.
Use Amazon CloudWatch Logs Insights to view the lineage.
B. Configure a workflow in AWS Step Functions that invokes the AWS Glue job and the Lambda functions. View the lineage in Step Functions Workflow Studio.
C. Deploy an Apache Airflow workflow that invokes the AWS Glue job and the Lambda functions.
View the lineage in the Airflow UI.
D. Deploy a workflow in AWS Step Functions to invoke the AWS Glue job. In the job code, invoke the Lambda functions before the job finishes. View the lineage from the AWS Glue UI.

Answer: B

Explanation:
AWS Step Functions is a fully serverless orchestration service that can coordinate an AWS Glue job and then invoke multiple Lambda functions after the job succeeds. It provides a single visual interface for the full workflow, making it easy to trace each step of the pipeline lineage in one place.

NEW QUESTION # 144
A Data Engineer enables a result cache at the session level with the following command:
ALTER SESSION SET USE CACHED RESULT = TRUE;
The Engineer then runs the following select query twice without delay:

The underlying table does not change between executions
What are the results of both runs?

A. The first and second run returned the same results because sample is deterministic
B. The first and second run returned differentresults because the query uses *instead of an explicit column list
C. The first and second run returned different results because the query is evaluated each time it is run.
D. The first and second run returned the same results, because the specific SEEDvalue was provided.

Answer: D

Explanation:
Explanation
The result cache is enabled at the session level, which means that repeated queries will return cached results if there is no change in the underlying data or session parameters. However, in this case, the result cache is not relevant because the query uses a specific SEED value for sampling, which makes it deterministic. Therefore, both runs will return the same results regardless of caching.

NEW QUESTION # 145
Let us say you have List of 50 Source files, which needs to be loaded into Snowflake internal stage. All these Source system files are already Brotli-compressed files. Which statement is correct with respect to Compression of Staged Files?

A. Snowflake automatically detect Brotli Compression, will skip further compression of all 50 files.
B. Even though Source files are already compressed, Snowflake do apply default gzip2 Compression to optimize the storage cost.
C. When staging 50 compressed files in a Snowflake stage, the files are automatically com-pressed using gzip.
D. Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.

Answer: D

Explanation:
Explanation
Auto-detection is not yet supported for Brotli-compressed files; when staging or loading Brotli-compressed files, you must explicitly specify the compression method that was used.
To Know more about Compression of Staged Files, please refer the link:
https://docs.snowflake.com/en/user-guide/intro-summary-loading.html#compression-of-staged-files

NEW QUESTION # 146
......

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