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ARA-C01 Questions and Answers
Question # 1
When using the copy into
command with the CSV file format, how does the match_by_column_name parameter behave?
A.
It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.
B.
The parameter will be ignored.
C.
The command will return an error.
D.
The command will return a warning stating that the file has unmatched columns.
Answer:
B
Explanation:
Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview]
The copy into
command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
When using the copy into
command with the CSV file format, the match_by_column_name parameter behaves as follows2:
References:
1: COPY INTO
| Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation
Question # 2
An Architect is integrating an application that needs to read and write data to Snowflake without installing any additional software on the application server.
How can this requirement be met?
A.
Use SnowSQL.
B.
Use the Snowpipe REST API.
C.
Use the Snowflake SQL REST API.
D.
Use the Snowflake ODBC driver.
Answer:
C
Explanation:
Explanation:
The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server. References: The answer can be verified from Snowflake’s official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:
Snowflake SQL REST API | Snowflake Documentation
Introduction to the SQL API | Snowflake Documentation
Submitting a Request to Execute SQL Statements | Snowflake Documentation
Question # 3
You are a snowflake architect in an organization. The business team came to to deploy an use case which requires you to load some data which they can visualize through tableau. Everyday new data comes in and the old data is no longer required.
What type of table you will use in this case to optimize cost
A.
TRANSIENT
B.
TEMPORARY
C.
PERMANENT
Answer:
A
Explanation:
Explanation:
A transient table is a type of table in Snowflake that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated1.
A temporary table is a type of table in Snowflake that is automatically dropped when the session ends or the current user logs out. Temporary tables do not incur any storage costs, but they are not visible to other users or sessions2.
A permanent table is a type of table in Snowflake that has a Fail-safe period and a Time Travel retention period of up to 90 days. Permanent tables are suitable for persistent and durable data that needs to be protected from accidental or malicious deletion3.
In this case, the use case requires loading some data that can be visualized through Tableau. The data is updated every day and the old data is no longer required. Therefore, the best type of table to use in this case to optimize cost is a transient table, because it does not incur any Fail-safe costs and it can have a short Time Travel retention period of 0 or 1 day. This way, the data can be loaded and queried by Tableau, and then deleted or overwritten without incurring any unnecessary storage costs.
References: : Transient Tables : Temporary Tables : Understanding & Using Time Travel
Question # 4
When loading data from stage using COPY INTO, what options can you specify for the ON_ERROR clause?
A.
CONTINUE
B.
SKIP_FILE
C.
ABORT_STATEMENT
D.
FAIL
Answer:
A, B, C
Explanation:
Explanation:
The ON_ERROR clause is an optional parameter for the COPY INTO command that specifies the behavior of the command when it encounters errors in the files. The ON_ERROR clause can have one of the following values1:
Therefore, options A, B, and C are correct.
References: : COPY INTO
Question # 5
What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?
A.
Every Kafka message is in JSON or Avro format.
B.
The default retention time for Kafka topics is 14 days.
C.
The Kafka connector supports key pair authentication, OAUTH. and basic authentication (for example, username and password).
D.
The Kafka connector will create one table and one pipe to ingest data for each topic. If the connector cannot create the table or the pipe it will result in an exception.
Answer:
D
Explanation:
Explanation:
The Snowflake Connector for Kafka is a Kafka Connect sink connector that reads data from one or more Apache Kafka topics and loads the data into a Snowflake table. The connector supports different authentication methods to connect to Snowflake, such as key pair authentication, OAUTH, and basic authentication (for example, username and password). The connector also supports different encryption methods, such as HTTPS and SSL1. The connector does not require that every Kafka message is in JSON or Avro format, as it can handle other formats such as CSV, XML, and Parquet2. The default retention time for Kafka topics is not relevant for the connector, as it only consumes the messages that are available in the topics and does not store them in Kafka. The connector will create one table and one pipe to ingest data for each topic by default, but this behavior can be customized by using the snowflake.topic2table.map configuration property3. If the connector cannot create the table or the pipe, it will log an error and retry the operation until it succeeds or the connector is stopped4. References:
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