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| Section | Objectives |
|---|---|
| Topic 1: Data Loading and Integration | - Bulk data loading
|
| Topic 2: Data Pipelines and Transformation | - Streams and Tasks
|
| Topic 3: Performance Optimization | - Query performance tuning
|
| Topic 4: Security, Governance, and Data Sharing | - Data governance
|
| Topic 5: Semi-Structured and Advanced Data Handling | - Semi-structured data processing
|
| Topic 6: Data Engineering Fundamentals on Snowflake | - Data ingestion concepts
|
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NEW QUESTION # 99
A retail company stores data from a product lifecycle management (PLM) application in an on- premises MySQL database. The PLM application frequently updates the database when transactions occur.
The company wants to gather insights from the PLM application in near real time. The company wants to integrate the insights with other business datasets and to analyze the combined dataset by using an Amazon Redshift data warehouse.
The company has already established an AWS Direct Connect connection between the on- premises infrastructure and AWS.
Which solution will meet these requirements with the LEAST development effort?
Answer: C
Explanation:
https://aws.amazon.com/ko/blogs/apn/change-data-capture-from-on-premises-sql-server-to- amazon-redshift-target/
NEW QUESTION # 100
1.+--------------------------------------------------------------+
2.| SYSTEM$CLUSTERING_INFORMATION('SF_DATA', '(COL1, COL3)') |
3.|--------------------------------------------------------------|
4.| { |
5.| "cluster_by_keys" : "(COL1, COL3)", |
6.| "total_partition_count" : 1156, |
7.| "total_constant_partition_count" : 0, |
8.| "average_overlaps" : 117.5484, |
9.| "average_depth" : 64.0701, |
10.| "partition_depth_histogram" : { |
11.| "00000" : 0, |
12.| "00001" : 0, |
13.| "00002" : 3, |
14.| "00003" : 3, |
15.| "00004" : 4, |
16.| "00005" : 6, |
17.| "00006" : 3, |
18.| "00007" : 5, |
19.| "00008" : 10, |
20.| "00009" : 5, |
21.| "00010" : 7, |
22.| "00011" : 6, |
23.| "00012" : 8, |
24.| "00013" : 8, |
25.| "00014" : 9, |
26.| "00015" : 8, |
27.| "00016" : 6, |
28.| "00032" : 98, |
29.| "00064" : 269, |
30.| "00128" : 698 |
31.| } |
32.| } |
33.+--------------------------------------------------------------+
The Above example indicates that the SF_DATA table is not well-clustered for which of following valid reasons?
Answer: E
NEW QUESTION # 101
Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?
Answer: A
Explanation:
Explanation
Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.
NEW QUESTION # 102
Can the same column be specified in both a Dynamic data masking policy signature and a row ac-cess policy signature at the same time?
Answer: A
NEW QUESTION # 103
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code.
Which solution will meet these requirements with the LEAST operational effort?
Answer: D
Explanation:
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.
NEW QUESTION # 104
......
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