Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

The metrics indicate data skew because one task is taking substantially longer than most tasks in the stage. Databricks advises checking the maximum task duration against the 75th-percentile duration: when the maximum is at least 50% higher, the stage may be skewed. Here, the 75th percentile is 50 seconds and the maximum is 80 seconds, so the maximum is 60% higher. The 25th percentile, median, and 75th percentile are tightly grouped between 40 and 50 seconds, while the maximum is a clear outlier. That pattern means a small number of tasks are processing disproportionately expensive partitions. These values alone do not prove cluster under-provisioning or excessive garbage collection; those diagnoses require resource, executor, spill, and GC metrics. Therefore, option A is correct.

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Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns: customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.

The use of INNER JOIN can be referenced from Databricks documentation on SQL JOIN or from other sources like W3Schools or GeeksforGeeks.