FAST DOWNLOAD TEST DEA-C01 PRACTICE | VERIFIED DEA-C01 PRINTABLE PDF: SNOWPRO ADVANCED: DATA ENGINEER CERTIFICATION EXAM

Fast Download Test DEA-C01 Practice | Verified DEA-C01 Printable PDF: SnowPro Advanced: Data Engineer Certification Exam

Fast Download Test DEA-C01 Practice | Verified DEA-C01 Printable PDF: SnowPro Advanced: Data Engineer Certification Exam

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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which are false statements about Star Schema?

  • A. Star schemas are denormalized.
  • B. Star schema is more flexible in terms of analytical needs compared to Data Vault Mod-elling.
  • C. The star schema separates business process data into facts, which hold the measurable, quantitative data about a business, and dimensions which are descriptive attributes re-lated to fact data.
  • D. The star schema is an important special case of the snowflake schema and is more effec-tive for handling simpler queries.

Answer: B


NEW QUESTION # 48
Which of the following statements is/are incorrect regarding Fail-safe data recovery?

  • A. Short-lived tables (i.e. <1 day), such as ETL work tables, can be defined as transient to eliminate Fail-safe costs.
  • B. Long-lived tables, such as fact tables, should always be defined as permanent to ensure they are fully protected by Fail-safe.
  • C. Data stored in temporary tables is not recoverable after the table is dropped as they do not have fail-safe.
  • D. If downtime and the time required to reload lost data are factors, permanent tables, even with their added Fail-safe costs, may offer a better overall solution than transient tables.
  • E. Historical data in transient tables can be recovered by Snowflake due to Operation fail-ure after the Time Travel retention period ends using Fail-safe.

Answer: E


NEW QUESTION # 49
Mark the incorrect statement in case Data engineer using the COPY INTO <table> command to load data from files into Snowflake tables?

  • A. For Local environment, Files are first copied ("staged") to an internal (Snowflake) stage, then loaded into a table.
  • B. For Data loading of files with semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-16.
  • C. UTF-32 & UTF-16 both encoding character sets supported for loading data from de-limited files (CSV, TSV, etc.)
  • D. For loading data from all semi-structured supported file formats (JSON, Avro, etc.), as well as unloading data, UTF-8 is the only supported character set.

Answer: B

Explanation:
Explanation
For Data Loading of delimited files (CSV, TSV, etc.), the default character set is UTF-8. To use any other characters sets, you must explicitly specify the encoding to use for loading.
For semi-structured file formats (JSON, Avro, etc.), the only supported character set is UTF-8.
Rest of the statements are correct.


NEW QUESTION # 50
A company uses Amazon RDS for MySQL as the database for a critical application. The database workload is mostly writes, with a small number of reads.
A data engineer notices that the CPU utilization of the DB instance is very high. The high CPU utilization is slowing down the application. The data engineer must reduce the CPU utilization of the DB Instance.
Which actions should the data engineer take to meet this requirement? (Choose two.)

  • A. Reboot the RDS DB instance once each week.
  • B. Use the Performance Insights feature of Amazon RDS to identify queries that have high CPU utilization. Optimize the problematic queries.
  • C. Implement caching to reduce the database query load.
  • D. Upgrade to a larger instance size.
  • E. Modify the database schema to include additional tables and indexes.

Answer: B,D


NEW QUESTION # 51
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

  • A. Use Amazon EMR and Apache Ranger.
  • B. Use a Hive metastore on an EMR cluster.
  • C. Use the AWS Glue Data Catalog.
  • D. Use a metastore on an Amazon RDS for MySQL DB instance.

Answer: C

Explanation:
https://aws.amazon.com/blogs/big-data/metadata-classification-lineage-and-discovery-using- apache-atlas-on-amazon-emr/


NEW QUESTION # 52
......

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