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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q78-Q83):

NEW QUESTION # 78
A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.
B. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.
C. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.
D. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

Answer: A

NEW QUESTION # 79
A company wants to migrate an application and an on-premises Apache Kafka server to AWS.
The application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The company wants to use the replatform migration strategy instead of the refactor strategy.
Which solution will meet these requirements with the LEAST management overhead?

A. Amazon Kinesis Data Firehose
B. Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless
C. Amazon Kinesis Data Streams
D. Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

Answer: B

NEW QUESTION # 80
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?

A. AWS DMS only supports full-load migrations, which would require downtime for the source database.
B. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.
C. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
D. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
E. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.

Answer: B

NEW QUESTION # 81
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?

A. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
B. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
C. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct)
D. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.
E. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.

Answer: C

Explanation:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.

NEW QUESTION # 82
Which one is not the Core benefits of micro-partitioning

A. Snowflake micro-partitions are derived automatically they do not need to be explicitly defined up-front or maintained by users.
B. Columns are stored independently within micro-partitions, often referred to as colum-nar storage.
C. Columns are also compressed individually within micro-partitions.
D. Micro-partitions can overlap in their range of values, helps data skewing.
E. Enables extremely efficient DML and fine-grained pruning for faster queries.

Answer: D

Explanation:
Explanation
The benefits of Snowflake's approach to partitioning table data include:
In contrast to traditional static partitioning, Snowflake micro-partitions are derived automatically; they don't need to be explicitly defined up-front or maintained by users.
As the name suggests, micro-partitions are small in size (50 to 500 MB, before compression), which enables extremely efficient DML and fine-grained pruning for faster queries.
Micro-partitions can overlap in their range of values, which, combined with their uniformly small size, helps prevent skew.
Columns are stored independently within micro-partitions, often referred to as columnar storage. This enables efficient scanning of individual columns; only the columns referenced by a query are scanned.
Columns are also compressed individually within micro-partitions. Snowflake automatically de-termines the most efficient compression algorithm for the columns in each micro-partition.

NEW QUESTION # 83
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