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DEA-C01 Test Guide | Authorized DEA-C01 Certification
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q120-Q125):
NEW QUESTION # 120
UDTFs also called a table function, returns zero, one, or multiple rows for each input row?
- A. YES
- B. NO
Answer: A
Explanation:
Explanation
UDFs may be scalar or tabular.
A scalar function returns one output row for each input row. The returned row consists of a single column/value.
A tabular function, also called a table function, returns zero, one, or multiple rows for each input row. A tabular UDF is defined by specifying a return clause that contains the TABLE keyword and specifies the names and data types of the columns in the table results. Tabular UDFs are often called UDTFs (user-defined table functions) or table UDFs.
NEW QUESTION # 121
Data Engineer, ran the below clustering depth analysis function:
select system$clustering_depth('TPCH_CUSTOMERS', '(C1, C6)', 'C9 = 30'); on TPCH_CUSTOMERS table, will return which of the following?
- A. An error: this function does not accept lists of columns as a third parameter.
- B. An error: this function does not accept predicates ('C9 = 30') as parameter.
- C. Calculate the clustering depth for a table using the clustering key defined for the table.
- D. Calculate the clustering depth for a table using mentioned columns in the table.
Answer: D
NEW QUESTION # 122
A data engineer wants to improve the performance of SQL queries in Amazon Athena that run against a sales data table.
The data engineer wants to understand the execution plan of a specific SQL statement. The data engineer also wants to see the computational cost of each operation in a SQL query.
Which statement does the data engineer need to run to meet these requirements?
- A. EXPLAIN ANALYZE SELECT * FROM sales;
- B. EXPLAIN SELECT * FROM sales;
- C. EXPLAIN ANALYZE FROM sales;
- D. EXPLAIN FROM sales;
Answer: A
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/athena-explain-statement.html
NEW QUESTION # 123
A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- B. Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- C. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.
- D. Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.
Answer: C
Explanation:
This solution directly triggers the Lambda function only when a .csv file is uploaded to the S3 bucket, minimizing unnecessary invocations of the Lambda function. It uses a specific event type (s3:ObjectCreated:*) and a filter rule to ensure that the Lambda function is invoked only for relevant events. Additionally, it directly invokes the Lambda function without the need for additional services like Amazon SNS, reducing operational overhead.
NEW QUESTION # 124
Which output is provided by both theSYSTEM$CLUSTERING_DEPTHfunction and theSYSTEM$CLUSTERING_INFORMATIONfunction?
- A. average_overlaps
- B. total_partition_count
- C. notes
- D. average_depth
Answer: D
Explanation:
Explanation
The output that is provided by both the SYSTEM$CLUSTERING_DEPTH function and the SYSTEM$CLUSTERING_INFORMATION function is average_depth. This output indicates the average number of micro-partitions that contain data for a given column value or combinationof column values. The other outputs are not common to both functions. The notes output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it contains additional information or recommendations about the clustering status of the table. The average_overlaps output is only provided by the SYSTEM$CLUSTERING_DEPTH function and it indicates the average number of micro-partitions that overlap with other micro-partitions for a given column value or combination of column values. The total_partition_count output is only provided by the SYSTEM$CLUSTERING_INFORMATION function and it indicates the total number of micro-partitions in the table.
NEW QUESTION # 125
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