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ARA-C01 PDF

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  • Exam Name: SnowPro Advanced: Architect Certification Exam
  • Last Update: 23-Apr-2024
  • Questions and Answers: 155
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ARA-C01 Questions and Answers

Question # 1

What Snowflake features should be leveraged when modeling using Data Vault?

A.

Snowflake’s support of multi-table inserts into the data model’s Data Vault tables

B.

Data needs to be pre-partitioned to obtain a superior data access performance

C.

Scaling up the virtual warehouses will support parallel processing of new source loads

D.

Snowflake’s ability to hash keys so that hash key joins can run faster than integer joins

Question # 2

An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO command.

Which configuration is valid?

A.

Location of files: Snowflake internal location

. File formats: CSV, XML

. File encoding: UTF-8

. Encryption: 128-bit

B.

Location of files: Amazon S3

. File formats: CSV, JSON

. File encoding: Latin-1 (ISO-8859)

. Encryption: 128-bit

C.

Location of files: Google Cloud Storage

. File formats: Parquet

. File encoding: UTF-8

· Compression: gzip

D.

Location of files: Azure ADLS

. File formats: JSON, XML, Avro, Parquet, ORC

. Compression: bzip2

. Encryption: User-supplied key

Question # 3

A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.

The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.

Which design will meet these requirements?

A.

Ingest the data using copy into and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

B.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

C.

Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

D.

Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Question # 4

How can an Architect enable optimal clustering to enhance performance for different access paths on a given table?

A.

Create multiple clustering keys for a table.

B.

Create multiple materialized views with different cluster keys.

C.

Create super projections that will automatically create clustering.

D.

Create a clustering key that contains all columns used in the access paths.

Question # 5

A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.

How can these requirements be met?

A.

Use ON_ERROR = continue in the copy into command.

B.

Use purge = TRUE in the copy into command.

C.

Use FURGE = FALSE in the copy into command.

D.

Use on error = SKIP_FILE in the copy into command.

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