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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
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| 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.
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| Topic 2 | - 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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| Topic 3 | - 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.
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| Topic 4 | - 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.
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| Topic 5 | - 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.
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q172-Q177):
NEW QUESTION # 172
A company receives marketing campaign data from a vendor. The company ingests the data into an Amazon S3 bucket every 40 to 60 minutes. The data is in CSV format. File sizes are between
100 KB and 300 KB.
A data engineer needs to set-up an extract, transform, and load (ETL) pipeline to upload the content of each file to Amazon Redshift.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use Amazon Redshift Spectrum to query the S3 bucket. Configure an AWS Glue Crawler for the S3 bucket to update metadata in an AWS Glue Data Catalog.
- B. Create an AWS Lambda function that connects to Amazon Redshift and runs a COPY command.
Use Amazon EventBridge to invoke the Lambda function based on an Amazon S3 upload trigger. - C. Create an Amazon Data Firehose stream. Configure the stream to use an AWS Lambda function as a source to pull data from the S3 bucket. Set Amazon Redshift as the destination.
- D. Creates an AWS Database Migration Service (AWS DMS) task. Specify an appropriate data schema to migrate. Specify the appropriate type of migration to use.
Answer: B
Explanation:
Configuring your S3 bucket to emit "Object Created" events into EventBridge and using a rule to invoke a lightweight Lambda function lets you automatically run a Redshift COPY for each new CSV. This serverless, event-driven pattern requires no continuously running infrastructure and has minimal operational overhead.
NEW QUESTION # 173
A company stores daily records of the financial performance of investment portfolios in .csv format in an Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.
The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.
Which solution will meet these requirements?
- A. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Specify a database name for the output.
- B. Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.
- C. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.
- D. Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler's data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.
Answer: A
Explanation:
https://docs.aws.amazon.com/glue/latest/dg/tutorial-add-crawler.html
NEW QUESTION # 174
Which ones are the false statements about Materialized Views?
- A. Materialized views are first-class account objects.
- B. Snowflake does not allow standard DML (e.g. INSERT, UPDATE, DELETE) on ma-terialized views.
- C. A materialized view can also be used as the data source for a subquery.
- D. Snowflake does not allow users to truncate materialized views.
- E. Clustering a subset of the materialized views on a table tends to be more cost-effective than clustering the table itself.
- F. Materialized views can be secure views.
Answer: A
Explanation:
Explanation
Materialized views are first-class Database objects & rest of the understandings are true.
NEW QUESTION # 175
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?
- A. Transition records to S3 Glacier Deep Archive storage after 30 days.
- B. Use S3 Standard-Infrequent Access (S3 Standard-IA) storage for all customer records.
- C. Apply a lifecycle policy to transition records to S3 Standard Infrequent-Access (S3 Standard-IA) storage after 30 days.
- D. Use S3 Intelligent-Tiering storage.
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 # 176
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)
- A. Split the data into files that are less than 10 KB.
- B. Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.
- C. Use file formats that are not splittable.
- D. Use a columnar storage file format.
- E. Partition the data based on the most common query predicates.
Answer: D,E
NEW QUESTION # 177
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