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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 (Q111-Q116):

NEW QUESTION # 111
A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3 bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularly proliferated to the S3 bucket. The data includes files that are in multiple formats.
The data engineer needs to automate the transfer process and must schedule the process to run periodically.
Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient way?

A. AWS Direct Connect
B. Amazon S3 Transfer Acceleration
C. AWS Glue
D. AWS DataSync

Answer: D

Explanation:
AWS DataSync is a managed data transfer service that simplifies and accelerates moving large amounts of data online between on-premises storage and Amazon S3, EFS, or FSx for Windows File Server. DataSync is optimized for efficient, incremental, and reliable transfers of large datasets, making it suitable for transferring 5 TB of data with daily updates.

NEW QUESTION # 112
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
AS SELECT *
FROM old_table;
B. CREATE TABLE new_table
LIKE old_table;
C. CREATE TABLE new_table
as SELECT *
FROM old_cable
WHERE 1=1;
D. CREATE TABLE new_table
AS SELECT *
FROM old_table
WITH NO DATA;

Answer: D

NEW QUESTION # 113
A data engineer is optimizing query performance in Amazon Athena notebooks that use Apache Spark to analyze large datasets that are stored in Amazon S3. The data is partitioned.
An AWS Glue crawler updates the partitions.
The data engineer wants to minimize the amount of data that is scanned to improve efficiency of Athena queries.
Which solution will meet these requirements?

A. Increase the frequency of AWS Glue crawler invocations to update the data catalog more often.
B. Configure Spark to use in-memory caching for frequently accessed data.
C. Apply partition filters in the queries.
D. Organize the data that is in Amazon S3 by using a nested directory structure.

Answer: C

Explanation:
By including predicates on your partition columns in each Athena query (for example, WHERE year = '2025' AND month = '05'), Athena prunes partitions at the optimizer level and reads only the matching S3 folders. This directly minimizes the data scanned and improves query efficiency with no additional infrastructure changes.

NEW QUESTION # 114
Streams record the differences between two offsets. If a row is added and then updated in the cur-rent offset, what will be the value of METADATA$ISUPDATE Columns in this scenario?

A. FALSE
B. UPDATE
C. INSERT
D. TRUE

Answer: A

Explanation:
Explanation
Stream Columns
A stream stores an offset for the source object and not any actual table columns or data. When que-ried, a stream accesses and returns the historic data in the same shape as the source object (i.e. the same column names and ordering) with the following additional columns:
METADATA$ACTION
Indicates the DML operation (INSERT, DELETE) recorded.
METADATA$ISUPDATE
Indicates whether the operation was part of an UPDATE statement. Updates to rows in the source object are represented as a pair of DELETE and INSERT records in the stream with a metadata column METADATA$ISUPDATE values set to TRUE.
METADATA$ROW_ID
Specifies the unique and immutable ID for the row, which can be used to track changes to specific rows over time.
Note that streams record the differences between two offsets. If a row is added and then updated in the current offset, the delta change is a new row. The METADATA$ISUPDATE row records a FALSE value.

NEW QUESTION # 115
How Data Engineer can do Monitoring of Files which are Staged Internally during Continuous data pipelines loading process? [Select all that apply]

A. Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days.
B. She can use the DATA_LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.
C. She can use the DATA_VALIDATE function to validate the data files She have loaded and can retrieve any errors encountered during the load.
D. She can Monitor the status of each COPY INTO command on the History tab page of the classic web interface.
E. She Can Monitor the files using Metadata maintained by Snowflake i.e. file-name,last_modified date etc.

Answer: A,D,E

Explanation:
Explanation
Monitoring Files Staged Internally
Snowflake maintains detailed metadata for each file uploaded into internal stage (for users, tables, and stages), including:
File name
File size (compressed, if compression was specified during upload)
LAST_MODIFIED date, i.e. the timestamp when the data file was initially staged or when it was last modified, whichever is later In addition, Snowflake retains historical data for COPY INTO commands executed within the pre-vious 14 days. The metadata can be used to monitor and manage the loading process, including de-leting files after upload completes:
Use the LIST command to view the status of data files that have been staged.
Monitor the status of each COPY INTO command on the History tab page of the classic web interface.
Use the VALIDATE function to validate the data files you've loaded and retrieve any errors en-countered during the load.
Use the LOAD_HISTORY Information Schema view to retrieve the history of data loaded into tables using the COPY INTO command.

NEW QUESTION # 116
......

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Will Nelson Will Nelson

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