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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Storage and compute separation 2. Virtual warehouses and compute scaling - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies
Data Loading and Integration	- Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows - Bulk data loading 1. COPY INTO command usage 2. File format handling
Security, Governance, and Data Sharing	- Access control 1. Role-based access control (RBAC) 2. Object-level permissions - Data governance 1. Secure data sharing concepts 2. Data masking policies
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
Performance Optimization	- Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms - Warehouse optimization 1. Concurrency management 2. Scaling strategies
Semi-Structured and Advanced Data Handling	- Semi-structured data processing 1. VARIANT data type usage 2. JSON, Avro, Parquet handling - Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q154-Q159):

NEW QUESTION # 154
A table is loaded using Snowpipe and truncated afterwards Later, a Data Engineer finds that the table needs to be reloaded but the metadata of the pipe will not allow the same files to be loaded again.
How can this issue be solved using the LEAST amount of operational overhead?

A. Wait until the metadata expires and then reload the file using Snowpipe
B. Recreate the pipe by using the create or replace pipe command
C. Modify the file by adding a blank row to the bottom and re-stage the file
D. Set the FORCE=TRUE option in the Snowpipe COPY INTO command

Answer: D

Explanation:
Explanation
The FORCE=TRUE option in the Snowpipe COPY INTO command allows Snowpipe to load files that have already been loaded before, regardless of the metadata. This is the easiest way to reload the same files without modifying them or recreating the pipe.

NEW QUESTION # 155
A company utilizes AWS Glue for ETL (Extract, Transform, Load) jobs to process large volumes of data stored in Amazon S3. Recently, they have been experiencing performance issues with their Glue jobs, resulting in longer processing times and delays in data delivery. Upon investigation, they found that the bottleneck occurred during data extraction from S3. Which of the following could be the potential cause of this performance issue?

A. There are issues with the IAM (Identity and Access Management) roles associated with the Glue job, leading to permission errors or delays in accessing S3 data.
B. The Glue job's configuration lacks optimization for parallelism, causing inefficient data extraction from S3, leading to longer processing times.
C. The size and structure of the S3 data files are causing overhead during data extraction, resulting in longer processing times for Glue jobs.
D. The underlying network bandwidth between AWS Glue and Amazon S3 is insufficient, causing slow data transfer rates and impacting job performance.

Answer: B

Explanation:
The bottleneck occurs during data extraction from S3, indicating a potential issue with the Glue job's configuration in this scenario. If the job is not optimized for parallelism, it may not efficiently utilize available resources, leading to slower data extraction. Optimizing the Glue job's configuration to enable parallel processing can improve the efficiency of data extraction from S3, reducing processing times.
Reference:
https://docs.aws.amazon.com/glue/latest/dg/optimize-spark-etl-job-performance.html

NEW QUESTION # 156
A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

A. AWS Glue Studio
B. AWS Glue workflows
C. AWS Step Functions
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: B

Explanation:
https://aws.amazon.com/blogs/big-data/orchestrate-an-etl-pipeline-using-aws-glue-workflows- triggers-and-crawlers-with-custom-classifiers/
https://aws.amazon.com/blogs/big-data/extracting-multidimensional-data-from-microsoft-sql- server-analysis-services-using-aws-glue/

NEW QUESTION # 157
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?

A. Most of the micro-partitions are grouped at the lower-end of the histogram, with the majority of micro-partitions having an overlap depth between 64 and 128.
B. Zero (0) constant micro-partitions out of 1156 total micro-partitions.
C. ALL of the above
D. High average of overlapping micro-partitions.
E. High average of overlap depth across micro-partitions.

Answer: C

NEW QUESTION # 158
A Data Engineer wants to create a new development database (DEV) as a clone of the permanent production database (PROD) There is a requirement to disable Fail-safe for all tables.
Which command will meet these requirements?

A. CREATE DATABASE DEV
CLOSE PROD
DATA_RETENTION_TIME_IN_DAYS =0L
B. CREATE DATABASE DEV
CLONE PROD
FAIL_SAFE=FALSE;
C. CREATE TRANSIENT DATABASE DEV
CLONE RPOD
D. CREATE DATABASE DEV
CLONE PROD;

Answer: C

Explanation:
Explanation
This option will meet the requirements of creating a new development database (DEV) as a clone of the permanent production database (PROD) and disabling Fail-safe for all tables. By using the CREATE TRANSIENT DATABASE command, the Data Engineer can create a transient database that does not have Fail-safe enabled by default. Fail-safe is a feature in Snowflake that provides additional protection against data loss by retaining historical data for seven days beyond the time travel retention period. Transient databases do not have Fail-safe enabled, which means that they do not incur additional storage costs for historical data beyond their time travel retention period. By using the CLONE option, the Data Engineer can create an exact copy of the PROD database, including its schemas, tables, views, and other objects.

NEW QUESTION # 159
......

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