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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q153-Q158):

NEW QUESTION # 153
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 AWS Glue DataBrew recipes to read and transform the CSV files.
C. Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
D. Use an AWS Glue custom crawler to read and transform the CSV files.

Answer: B

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 # 154
Charles, A Lead Data engineer, with ACCOUNTADMIN role wants to configure the time travel for one of the Schema's object. He setup the MIN_DATA_RETENTION_TIME_IN_DAYS pa-rameter with Value 79 at account level but he figured out that DA-TA_RETENTION_TIME_IN_DAYS is already set with value 81 at account level. What would be the effective minimum data retention period for an object?

A. There is no such MIN_DATA_RETENTION_TIME_IN_DAYS parameter
B. 0
C. 1
D. 2

Answer: C

Explanation:
Explanation
A user with the ACCOUNTADMIN role can also set the MIN_DATA_RETENTION_TIME_IN_DAYS at the account level. This parameter setting enforc-es a minimum data retention period for databases, schemas, and tables. Setting MIN_DATA_RETENTION_TIME_IN_DAYS does not alter or replace the DA-TA_RETENTION_TIME_IN_DAYS parameter value. It may, however, change the effective data retention period for objects. When MIN_DATA_RETENTION_TIME_IN_DAYS is set at the ac-count level, the data retention period for an object is determined by MAX(DATA_RETENTION_TIME_IN_DAYS, MIN_DATA_RETENTION_TIME_IN_DAYS).

NEW QUESTION # 155
When would a Data engineer use table with the flatten function instead of the lateral flatten combination?

A. When TABLE with FLATTENrequires another source in the from clause to refer to
B. WhenTABLE with FLATTENrequires no additional source m the from clause to refer to
C. When table withFLATTENis acting like a sub-query executed for each returned row
D. Whenthe LATERALFLATTENcombination requires no other source m the from clause to refer to

Answer: A

Explanation:
Explanation
The TABLE function with the FLATTEN function is used to flatten semi-structured data, such as JSON or XML, into a relational format. The TABLE function returns a table expression that can be used in the FROM clause of a query. The TABLE function with the FLATTEN function requires another source in the FROM clause to refer to, such as a table, view, or subquery that contains the semi-structured data. For example:
SELECT t.value:city::string AS city, f.value AS population FROM cities t, TABLE(FLATTEN(input => t.value:population)) f; In this example, the TABLE function with the FLATTEN function refers to the cities table in the FROM clause, which contains JSON data in a variant column named value. The FLATTEN function flattens the population array within each JSON object and returns a table expression with two columns: key and value.
The query then selects the city and population values from the table expression.

NEW QUESTION # 156
A data engineer needs to create an empty copy of an existing table in Amazon Athena to perform data processing tasks. The existing table in Athena contains 1,000 rows.
Which query will meet this requirement?

A. CREATE TABLE new_table
LIKE old_table;
B. CREATE TABLE new_table
AS SELECT *
FROM old_table
WITH NO DATA;
C. CREATE TABLE new_table
AS SELECT *
FROM old_table;
D. CREATE TABLE new_table
as SELECT *
FROM old_cable
WHERE 1=1;

Answer: B

NEW QUESTION # 157
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

A. Use the AWS Glue Spark shuffle manager.
B. Convert the sort-merge join in the job to be a broadcast join.
C. Deploy are Amazon Elastic Block Store (Amazon EBS) volume for the job to use.
D. Convert the DataFrames to DynamicFrames, and perform a DynamicFrame join in the job.

Answer: A

Explanation:
The error "No space left on device" during a sort-merge join typically results from insufficient disk space during the shuffle phase of a Spark job. Using the AWS Glue Spark shuffle manager (optimized shuffle manager) helps reduce disk usage and improves shuffle performance by optimizing how intermediate data is written and read. This directly addresses the issue without requiring job logic changes or manual resource management.

NEW QUESTION # 158
......

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