ARA-C01 Data Engineering — 15 Questions

Question 1

An architect is designing a high-volume ingestion pipeline using Snowpipe for an S3 bucket. The data contains complex nested JSON structures that need to be flattened before being inserted into a final reporting table. Which TWO strategies will optimize the performance and cost of this pipeline?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Perform the FLATTEN and transformation logic directly within the COPY INTO statement of the Snowpipe definition.. Optimizing Snowpipe involves balancing the frequency of loads with the size of the files being ingested to ensure efficient use of the serverless compute resources. By performing initial transformations like flattening and filtering within the COPY INTO statement, the architect can reduce the volume of data moved through the pipeline and minimize subsequent processing steps.

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Configure the S3 event notifications to trigger Snowpipe for every individual file smaller than 10MB to ensure low latency.

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Increase the size of the virtual warehouse assigned to the Snowpipe to handle the complex flattening operations.

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Implement a MATERIALIZED VIEW on top of the raw landing table to handle the JSON flattening automatically.

Question 2

Refer to the exhibit. An architect observes that the Dynamic Table (DT_SALES_AGG) is not refreshing every minute as expected, even though new data is consistently arriving in the RAW_SALES table. What is the most likely cause of this behavior?

Accepted Answer

The RAW_SALES table is a standard table without Change Tracking enabled, preventing incremental refreshes.. Dynamic Tables depend on the refresh frequency of their upstream sources and the availability of warehouse resources. If the upstream table is also a Dynamic Table with a longer lag, or if the warehouse is suspended and the refresh does not trigger it due to specific configuration, the target lag may not be met. Additionally, TARGET_LAG is a maximum desired delay, not a guaranteed execution interval.

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The warehouse 'COMPUTE_WH' is suspended and Dynamic Tables cannot automatically resume a warehouse for scheduled refreshes.

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The TARGET_LAG of 1 minute is too short for Snowflake to calculate the group-by aggregation, causing a skip.

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The DYNAMIC_TABLE_REFRESH_MODE is explicitly set to FULL, which overrides the TARGET_LAG setting for frequent updates.

Question 3

A company is using an External Table to query data stored in an S3 bucket partitioned by date (/year/month/day/). They notice that queries are scanning the entire bucket instead of just the relevant partitions. Which action should the architect take to resolve this?

Accepted Answer

Recreate the External Table using the PARTITION BY clause and use the metadata$external_table_partition column in queries.. For External Tables, Snowflake does not automatically discover partitions unless the metadata is refreshed and the query explicitly uses the partition columns defined in the DDL. Partitioning is essential for performance in external tables as it allows the query engine to prune files, significantly reducing the amount of data scanned and improving query response times.

Answer

Enable the AUTO_REFRESH property on the External Table and ensure the S3 event notifications are configured.

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Convert the External Table into a Materialized View to take advantage of automatic clustering and pruning.

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Run the ALTER EXTERNAL TABLE ... REFRESH command before every query to force the engine to recognize new files.

Question 4

A data engineering team needs to configure the Snowflake Kafka Connector to ingest data into a landing table. Which THREE parameters are required in the Kafka configuration file to establish a successful connection and data flow?

Accepted Answer

snowflake.url.name. The Snowflake Kafka Connector requires specific authentication and destination parameters to function correctly. This includes the Snowflake account identifier, the specific user created for the connector, and the private key used for key-pair authentication, ensuring a secure and authorized connection between the Kafka cluster and the Snowflake environment.

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snowflake.warehouse.name

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snowflake.role.name

Question 5

You are building a pipeline that processes high-velocity IoT data. You need to provide a real-time dashboard with low-latency requirements. The raw data is stored in a table with billions of rows, and the dashboard queries involve highly selective filters on a specific device ID and timestamp. Which feature should you implement to minimize query latency?

Accepted Answer

Enable Search Optimization on the table for the device ID and timestamp columns.. For high-concurrency, low-latency queries on very large tables with selective filters, the Search Optimization Service (SOS) is the most effective solution. Unlike materialized views, which store pre-computed results, SOS creates a persistent data structure that allows the query optimizer to quickly locate specific rows based on equality or range predicates.

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Create a Materialized View that aggregates data by device ID and hour.

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Define a Clustering Key on the device ID column to ensure data is physically sorted.

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Use a Result Set Cache by ensuring the dashboard queries are identical across users.

Question 6

An architect is implementing a solution to process unstructured PDF documents stored in an internal stage. The goal is to allow a third-party application to securely access these files for a limited time without providing Snowflake credentials. Which Snowflake feature is best suited for this?

Accepted Answer

Use the BUILD_SCOPED_FILE_URL function to create temporary access links for the files.. Scoped URLs provide temporary, secure access to files in a stage. They are generated using the BUILD_SCOPED_FILE_URL function and are valid for the duration of the current session. This is ideal for sharing unstructured data with external applications or users via a web interface without exposing underlying storage credentials.

Answer

Generate a Pre-signed URL using the GET_PRESIGNED_URL function for each PDF file.

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Create a Directory Table on the stage and grant SELECT privileges to the third-party application.

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Configure a Storage Integration with a public S3 bucket and provide the application with the S3 URI.

Question 7

An architect needs to enable Change Data Capture (CDC) on a Secure View that joins multiple tables. Which TWO requirements must be met to create a Stream on this view?

Accepted Answer

Change tracking must be enabled on all underlying base tables included in the view definition.. Creating a stream on a view requires that the underlying tables have change tracking enabled and that the view itself is structured in a way that allows Snowflake to track row-level changes. This is more complex than a stream on a table because the system must track changes across all joined components of the view.

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The view must be a Materialized View to support persistent change tracking.

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The view must be defined using only inner joins and cannot contain any window functions.

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The underlying tables must all reside within the same database as the view itself.

Question 8

An architect is designing a multi-step ETL process using Snowflake Scripting. They need to ensure that if any step fails, all previous steps in that session are rolled back, and a record of the failure is inserted into a persistent log table. What is the best strategy?

Accepted Answer

Begin a transaction, use a TRY...CATCH block, and in the CATCH, execute ROLLBACK then call a second procedure with its own transaction to log.. Snowflake Scripting supports EXCEPTION blocks for error handling. To ensure rollback, the script should start an explicit transaction. If an error occurs, the EXCEPTION block can execute a ROLLBACK command. However, since the rollback would also remove the log entry, the logging must happen after the rollback or in a separate transaction.

Answer

Use a single TRANSACTION block and place the logging statement inside the EXCEPTION handler before the ROLLBACK command.

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Use an EXCEPTION block to catch errors, execute ROLLBACK, and then use a separate Task to log the failure.

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Set the parameter TRANSACTION_ABORT_ON_ERROR = TRUE to automatically handle the rollback and logging.

Question 9

A financial institution processes high-volume clickstream data partitioned by date in cloud object storage. They plan to use external tables in Snowflake to query this data without loading it. However, users report poor query performance when filtering by date. What optimization technique should a data architect implement to resolve this issue?

Accepted Answer

Define virtual columns using the METADATA$FILENAME function and apply partition pruning to align with the storage path.. External tables do not store data natively in Snowflake micro-partitions, meaning queries often require full table scans unless metadata columns are leveraged. Implementing virtual columns and partition pruning enables Snowflake to skip unnecessary files, drastically accelerating scan performance for large external datasets.

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Create a clustering key directly on the external table definition using the underlying cloud storage path expression.

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Execute an ALTER EXTERNAL TABLE REFRESH command every hour to cache file metadata into local warehouse memory.

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Convert the external table into a transient table using an automatic stream-based task running every five minutes.

Question 10

Refer to the exhibit. An architect is reviewing this COPY command for a production data load. What is the specific behavior of the ON_ERROR and PURGE parameters in this context?

Accepted Answer

Files with errors are skipped, and only successfully loaded files are deleted from the stage.. The COPY INTO command is the primary method for bulk loading data from external stages. Using the PATTERN attribute allows for granular file selection, while ON_ERROR='CONTINUE' ensures that individual file failures do not halt the process. PURGE='TRUE' manages the external stage by removing files after a successful load.

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The load will fail if any file contains an error, and no files will be deleted.

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All files matching the pattern are deleted from the stage regardless of load success.

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The statement will stop at the first error but still delete the files loaded before the error.

Question 11

A healthcare company needs to process large PDF medical reports stored in an internal stage. They want to use a Python-based Snowpark procedure to extract text and store it in a table. How should the architect provide access to these files for the Snowpark procedure?

Accepted Answer

Use the BUILD_SCOPED_FILE_URL function to generate a temporary URL. Processing unstructured data in Snowflake often involves Snowpark and Scoped URLs. Scoped URLs provide a secure, temporary way to access files during the scope of a session or procedure. This ensures that the Python logic can read the binary data from the stage without exposing the entire stage permanently.

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Pass the absolute S3 path to the Snowpark procedure

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Grant the OWNERSHIP privilege on the stage to the Snowpark procedure's role

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Convert the PDF files to Base64 strings and store them in a VARIANT column first

Question 12

An architect is evaluating the use of Materialized Views to optimize a dashboard. Which THREE factors should be considered as potential limitations or costs? (Select THREE)

Accepted Answer

They do not support joins between two or more tables.. Materialized Views provide performance benefits but come with specific constraints. They are restricted to a single table, incur background maintenance costs, and do not support complex SQL features like window functions. Understanding these trade-offs is vital for choosing between Materialized Views, Dynamic Tables, or Search Optimization.

Answer

They cannot be created on a base table that has a Clustering Key.

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They must be manually refreshed using a TASK or a stored procedure.

Question 13

A fact table containing 50TB of data is experiencing slow query performance for filters on 'transaction_date' and 'region_id'. The data is loaded daily in chronological order. How should the architect optimize this table for these specific filters?

Accepted Answer

Define a Clustering Key on (transaction_date, region_id). For very large tables, Snowflake's natural ingestion order often provides good clustering on time. However, when queries frequently filter by other dimensions like region_id, explicit clustering is needed. Defining a clustering key helps Snowflake prune micro-partitions more effectively, reducing the amount of data scanned and improving performance.

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Create a Search Optimization Service index on the 'region_id' column

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Re-sort the data manually before every daily load using an ORDER BY clause

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Split the fact table into multiple smaller tables based on the 'region_id'

Question 14

A data architect is moving a legacy ETL process to Snowflake Tasks. Some tasks run for only 10 seconds and occur every minute. Which warehouse configuration would be most cost-effective for these short-duration, frequent tasks?

Accepted Answer

A Task defined without a WAREHOUSE parameter to use serverless compute. For short-lived, frequent tasks, serverless compute is often more cost-effective than a standard virtual warehouse. With a standard warehouse, the minimum billing is 60 seconds, and the warehouse may stay active for a 'scaling policy' period. Serverless tasks bill by the second of actual execution time, eliminating idle cost.

Answer

A standard X-Small warehouse with an Auto-Suspend of 60 seconds

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A Multi-cluster warehouse with the 'Economy' scaling policy

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A dedicated warehouse for each individual task in the DAG

Question 15

An architect is configuring a Directory Table for an external stage in Azure. Which TWO statements are true regarding the management and refresh of this directory table? (Select TWO)

Accepted Answer

Manual refreshes are performed using the ALTER STAGE ... REFRESH command.. Directory tables provide a queryable catalog of files. They can be refreshed manually or automatically. Manual refreshes are useful for batch updates, while auto-refreshes leverage cloud event notifications (like Azure Event Grid) to keep the metadata current without manual intervention.

Answer

The directory table is automatically refreshed every time it is queried.

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Refreshes for directory tables are free and do not consume credits.

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Directory tables can only be enabled for internal Snowflake stages.