Databricks-Certified-Data-Engineer-Professional 무료 덤프문제 온라인 액세스

The view updates represents an incremental batch of all newly ingested data to be inserted or Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

• A.The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
• B.The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
• C.The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
• D.The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

• A.Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
• B.Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
• C.Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
• D.Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
• E.Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.

Which statement describes Delta Lake optimized writes?

• A.A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
• B.Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
• C.Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
• D.An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

• A.Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
• B.Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
• C.Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
• D.Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

• A.All records are cached to attached storage and then the filter is applied Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
• B.The Delta log is scanned for min and max statistics for the latitude column
• C.All records are cached to an operational database and then the filter is applied
• D.The Hive metastore is scanned for min and max statistics for the latitude column
• E.The Parquet file footers are scanned for min and max statistics for the latitude column