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| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Engineer Certification Exam |
| Exam Number: | DEA-C01 |
| Real Exam Qty: | approximately 50 questions |
| Exam Format: | Multiple Select, Multiple Choice |
| Related Certifications: | SnowPro Core Certification (Recommended Prerequisite) |
| Certificate Validity Period: | 2 years (recertification required) |
| Exam Duration: | 90 minutes |
| Exam Price: | $375 USD |
| Passing Score: | ~70% (exact passing score not publicly disclosed by Snowflake) |
| Available Languages: | English |
| 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 # 97
Mark a Data Engineer, looking to implement streams on local views & want to use change tracking metadata for one of its Data Loading use case. Please select the incorrect understanding points of Mark with respect to usage of Streams on Views?
Answer: C
Explanation:
Explanation
A stream object records data manipulation language (DML) changes made to tables, including in-serts, updates, and deletes, as well as metadata about each change, so that actions can be taken us-ing the changed data. This process is referred to as change data capture (CDC). An individual table stream tracks the changes made to rows in a source table. A table stream (also referred to as simply a "stream") makes a "change table" available of what changed, at the row level, between two transac-tional points of time in a table. This allows querying and consuming a sequence of change records in a transactional fashion.
Streams can be created to query change data on the following objects:
Standard tables, including shared tables.
Views, including secure views
Directory tables
External tables
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 the state 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. When the first stream for a table is created, a pair of hidden columns are added to the source table and begin storing change tracking metadata. These columns consume a small amount of storage. The CDC records returned when querying a stream rely on a combination of the offset stored in the stream and the change tracking metadata stored in the table. Note that for streams on views, change tracking must be enabled explicitly for the view and underlying tables to add the hidden columns to these tables.
Streams on views support both local views and views shared using Snowflake Secure Data Sharing, including secure views. Currently, streams cannot track changes in materialized views.
Views with the following operations are not yet supported:
GROUP BY clauses
QUALIFY clauses
Subqueries not in the FROM clause
Correlated subqueries
LIMIT clauses
Change Tracking:
Change tracking must be enabled in the underlying tables.
Prior to creating a stream on a view, you must enable change tracking on the underlying tables for the view.
Set the CHANGE_TRACKING parameter when creating a view (using CREATE VIEW) or later (using ALTER VIEW).
As an alternative to streams, Snowflake supports querying change tracking metadata for tables or views using the CHANGES clause for SELECT statements. The CHANGES clause enables query-ing change tracking metadata between two points in time without having to create a stream with an explicit transactional offset.
NEW QUESTION # 98
A company stores sensitive data in an Amazon Redshift table. The company needs to give specific users the ability to access the sensitive data. The company must not create duplication in the data.
Customer support users must be able to see the last four characters of the sensitive data. Audit users must be able to see the full value of the sensitive data. No other users can have the ability to access the sensitive information.
Which solution will meet these requirements?
Answer: B
Explanation:
Amazon Redshift's dynamic data masking lets you define masking policies on sensitive columns without duplicating data. You attach a masking policy that exposes only the last four characters to customer support IAM roles, while audit IAM roles see the full values. This meets the requirement for role-based column-level access with no data duplication.
NEW QUESTION # 99
Mark the Correct Statements for the VALIDATION_MODE option used by Data Engineer for Da-ta loading operations in his/her COPY INTO <table> command:
Answer: A,B,D
Explanation:
Explanation
All the Statements are correct except the statement saying VALIDATION_MODE only support Data loading operation.
VALIDATION_MODE can be used with COPY INTO <location> command as well i.e for data unloading operation.
VALIDATION_MODE = RETURN_ROWS can be used at the time of Data unloading.
This option instructs the COPY command to return the results of the query in the SQL statement instead of unloading the results to the specified cloud storage location. The only supported valida-tion option is RETURN_ROWS. This option returns all rows produced by the query.
When you have validated the query, you can remove the VALIDATION_MODE to perform the unload operation.
NEW QUESTION # 100
Which one is not the Core benefits of micro-partitioning
Answer: E
Explanation:
Explanation
The benefits of Snowflake's approach to partitioning table data include:
In contrast to traditional static partitioning, Snowflake micro-partitions are derived automatically; they don't need to be explicitly defined up-front or maintained by users.
As the name suggests, micro-partitions are small in size (50 to 500 MB, before compression), which enables extremely efficient DML and fine-grained pruning for faster queries.
Micro-partitions can overlap in their range of values, which, combined with their uniformly small size, helps prevent skew.
Columns are stored independently within micro-partitions, often referred to as columnar storage. This enables efficient scanning of individual columns; only the columns referenced by a query are scanned.
Columns are also compressed individually within micro-partitions. Snowflake automatically de-termines the most efficient compression algorithm for the columns in each micro-partition.
NEW QUESTION # 101
Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).
Answer: B,E
Explanation:
Explanation
The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:
HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.
HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
NEW QUESTION # 102
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