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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Available Languages: | English |
| Passing Score: | ~70% (exact passing score not publicly disclosed by Snowflake) |
| Real Exam Qty: | approximately 50 questions |
| Exam Duration: | 90 minutes |
| Related Certifications: | SnowPro Core Certification (Recommended Prerequisite) |
| Exam Format: | Multiple Select, Multiple Choice |
| Exam Price: | $375 USD |
| Certificate Validity Period: | 2 years (recertification required) |
| Sample Questions: | Snowflake DEA-C01 Sample Questions |
| Exam Way: | Online proctored exam (via Pearson VUE) or in-person testing center |
| Pre Condition: | SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential. |
| Official Syllabus URL: | https://www.snowflake.com/certification/ |
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NEW QUESTION # 283
Which are the two ways to access elements in a JSON object?
Answer: A,D
NEW QUESTION # 284
Which Scenario Data engineer decide Materialized views are not useful. Select All that apply.
Answer: B
Explanation:
Explanation
A materialized view is a pre-computed data set derived from a query specification (the SELECT in the view definition) and stored for later use. Because the data is pre-computed, querying a material-ized view is faster than executing a query against the base table of the view. This performance dif-ferencecan be significant when a query is run frequently or is sufficiently complex. As a result, ma-terialized views can speed up expensive aggregation, projection, and selection operations, especially those that run frequently and that run on large data sets.
Materialized views require Enterprise Edition.
Materialized views are designed to improve query performance for workloads composed of com-mon, repeated query patterns. However, materializing intermediate results incurs additional costs. As such, before creating any materialized views, you should consider whether the costs are offset by the savings from re-using these results frequently enough.
Materialized views are particularly useful when:
Query results contain a small number of rows and/or columns relative to the base table (the table on which the view is defined).
Query results contain results that require significant processing, including:
1. Analysis of semi-structured data.
2. Aggregates that take a long time to calculate.
The query is on an external table (i.e. data sets stored in files in an external stage), which might have slower performance compared to querying native database tables.
The view's base table does not change frequently.
NEW QUESTION # 285
John, Data Engineer, do have technical requirements to refresh the External tables Metadata period-ically or in auto mode, which approach John can take to meet this technical specification?
Answer: C,D
Explanation:
Explanation
Both Option A & B are correct.
For Refreshing External Table Metadata on a Auto Mode, John can use the AUTO_REFRESH pa-rameter properties of External table.When an external table is created, the AUTO_REFRESH pa-rameter is set to TRUE by default.
When an external table is created, the AUTO_REFRESH parameter is set to TRUE by default.
Snowflake recommend that you accept this default value for external tables that reference data files in either Amazon S3 or Microsoft Azure stages.
However, the automatic refresh option is not available currently for external tables that reference Google Cloud Storage stages.
For these external tables, manually refreshing the metadata on a schedule can be useful.
The following example refreshes the metadata for an external table named snowdb.snowschema.snow_ext_table (using ALTER EXTERNAL TABLE ... REFRESH) on a schedule.
-- Create a task that executes an ALTER EXTERNAL TABLE ... REFRESH statement every 5 minutes.
1.CREATE TASK snow_ext_table_refresh_task
2.WAREHOUSE=mywh
3.SCHEDULE='5 minutes'
4.AS
5.ALTER EXTERNAL TABLE snowmydb.snowmyschema.snow_ext_table REFRESH;
NEW QUESTION # 286
A company's application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team's cost and performance optimization requirements. Which solution will meet these requirements?
Answer: D
Explanation:
Amazon OpenSearch Service is built for near real-time search and analytics with low query latency, making it a strong fit for applications that must handle about 1,000 requests per second.
Giving each data team its own OpenSearch cluster lets each team independently tune storage, indexing, scaling, and cost/performance settings to match its own workload requirements.
Reference:
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/bp.html
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/serverless- overview.html
NEW QUESTION # 287
To advance the offset of a stream to the current table version without consuming the change data in a DML operation, which of the following operations can be done by Data Engineer? [Select 2]
Answer: A,D
Explanation:
Explanation
When created, a stream logically takes an initial snapshot of every row in the source object (e.g. ta-ble, external table, or the underlying tables for a view) by initializing a point in time (called an off-set) as the current transactional version of the object. The change tracking system utilized by the stream then records information about the DML changes after this snapshot was taken. Change rec-ords provide thestate of a row before and after the change. Change information mirrors the column structure of the tracked source object and includes additional metadata columns that describe each change event.
Note that a stream itself does not contain any table data. A stream only stores an offset for the source object and returns CDC records by leveraging the versioning history for the source object.
A new table version is created whenever a transaction that includes one or more DML statements is committed to the table.
In the transaction history for a table, a stream offset is located between two table versions. Query-ing a stream returns the changes caused by transactions committed after the offset and at or before the current time.
Multiple queries can independently consume the same change data from a stream without changing the offset.
A stream advances the offset only when it is used in a DML transaction. This behavior applies to both explicit and autocommit transactions. (By default, when a DML statement is execut-ed, an autocommit transaction is implicitly started and the transaction is committed at the comple-tion of the statement. This behavior is controlled with the AUTOCOMMIT parameter.) Querying a stream alone does not advance its offset, even within an explicit transaction; the stream contents must be consumed in a DML statement.
To advance the offset of a stream to the current table version without consuming the change data in a DML operation, complete either of the following actions:
Recreate the stream (using the CREATE OR REPLACE STREAM syntax).
Insert the current change data into a temporary table. In the INSERT statement, query the stream but include a WHERE clause that filters out all of the change data (e.g. WHERE 0 = 1).
NEW QUESTION # 288
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