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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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

NEW QUESTION # 202
A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format.
The company is experiencing slow query performance.
Which solutions will improve query performance? (Choose two.)

Answer: B,D

Explanation:
Compaction reduces many small Parquet files into larger ones, lowering file-listing and open/scan overhead for faster reads.
Copy-on-write favors read performance by materializing updates into rewritten data files, resulting in more contiguous, scan-efficient files for queries.


NEW QUESTION # 203
Company DEF has a strict security policy that mandates that all data at rest in Amazon S3 must be encrypted. They want to ensure that the encryption keys are managed by AWS, but they also want the flexibility to change the encryption keys when required.
Which of the following encryption methods best meets Company DEF's requirements?

Answer: B


NEW QUESTION # 204
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?

Answer: C

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 # 205
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?

Answer: C

Explanation:
Amazon S3 Storage Lens provides a comprehensive view of S3 storage usage and activity. It includes metrics such as the number of incomplete multipart uploads and the number of non- current object versions, making it an ideal tool for gathering insights into storage inefficiencies like outdated versions and incomplete uploads. This solution requires minimal operational effort as it is built into S3 and provides a user-friendly dashboard with detailed insights.
Using the AWS CLI to manually list and check multipart uploads and object versions would require scripting and regular execution, leading to higher operational overhead.
S3 Inventory provides reports on the objects in your bucket, but it does not directly provide information on incomplete multipart uploads or outdated versions. You would need to process the data further to gather the specific insights.
AWS usage reports are more focused on billing and usage rather than detailed storage optimization metrics like incomplete multipart uploads and outdated object versions.


NEW QUESTION # 206
The company stores a large volume of customer records in Amazon S3. To comply with regulations, the company must be able to access new customer records immediately for the first
30 days after the records are created. The company accesses records that are older than 30 days infrequently.
The company needs to cost-optimize its Amazon S3 storage.
Which solution will meet these requirements MOST cost-effectively?

Answer: C

Explanation:
S3 Standard-IA (Infrequent Access) is ideal for data that is accessed less frequently but requires immediate access when needed. Since the company needs immediate access to customer records for the first 30 days and infrequent access afterward, applying an S3 lifecycle policy to automatically transition the data to S3 Standard-IA after 30 days provides a cost-effective solution. This way, records remain easily accessible during the initial 30 days and are moved to a cheaper storage tier when they are accessed less often.
S3 Intelligent-Tiering automatically moves data between frequent and infrequent access tiers based on access patterns, but it incurs additional monitoring and automation charges. This option is typically more expensive compared to S3 Standard-IA if the access pattern is well understood (like in this case, where data is accessed frequently for the first 30 days and infrequently thereafter).
S3 Glacier Deep Archive is a low-cost storage option for archival data that is rarely accessed and requires hours for retrieval. It does not meet the requirement for immediate access to records after 30 days.
Storing all data in S3 Standard-IA is more expensive during the first 30 days when the data is accessed frequently. S3 Standard is more cost-effective for frequently accessed data during that period.


NEW QUESTION # 207
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