The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id. Which statement describes what the number alongside this field represents?
A. The job_id is returned in this field.
B. The job_id and number of times the job has been are concatenated and returned.
C. The number of times the job definition has been run in the workspace.
D. The globally unique ID of the newly triggered run.
Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id. While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly. <br>
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org. Which of the following solutions addresses the situation while emphasizing simplicity?
A. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
B. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
C. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
D. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
References:
• Databricks documentation on using SQL views in Delta Lake: https://docs.databricks.com/delta/quick-start.html#sql-views
A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?
A. The five Minute Load Average remains consistent/flat
B. Bytes Received never exceeds 80 million bytes per second
C. Total Disk Space remains constant
D. Network I/O never spikes
E. Overall cluster CPU utilization is around 25%
Explanation:
This is the correct answer because it indicates a bottleneck caused by code executing on the driver. A bottleneck is a situation where the performance or capacity of a system is limited by a single component or resource. A bottleneck can cause slow execution, high latency, or low throughput. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor. When evaluating the Ganglia Metrics for this cluster, one can look for indicators that show how the cluster resources are being utilized, such as CPU, memory, disk, or network. If the overall cluster CPU utilization is around 25%, it means that only one out of the four nodes (driver + 3 executors) is using its full CPU capacity, while the other three nodes are idle or underutilized. This suggests that the code executing on the driver is taking too long or consuming too much CPU resources, preventing the executors from receiving tasks or data to process. This can happen when the code has driver-side operations that are not parallelized or distributed, such as collecting large amounts of data to the driver, performing complex calculations on the driver, or using non-Spark libraries on the driver.
Verified References: [Databricks Certified Data Engineer Professional], under “Spark Core” section; Databricks Documentation, under “View cluster status and event logs - Ganglia metrics” section; Databricks Documentation, under “Avoid collecting large RDDs” section.
In a Spark cluster, the driver node is responsible for managing the execution of the Spark application, including scheduling tasks, managing the execution plan, and interacting with the cluster manager. If the overall cluster CPU utilization is low (e.g., around 25%), it may indicate that the driver node is not utilizing the available resources effectively and might be a bottleneck.
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.
Given the current implementation, which method can be used?
A. Parse the Delta Lake transaction log to identify all newly written data files.
B. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
C. Execute a query to calculate the difference between the new version and the previous version using Delta Lake’s built-in versioning and time travel functionality.
D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.
References:
• Delta Lake Documentation on Time Travel: Delta Time Travel
• Delta Lake Versioning: Delta Lake Versioning Guide
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used. Which strategy will yield the best performance without shuffling data?
A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
D. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
Explanation:
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly.
• Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
• Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
• Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB.
• The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).
References:
• Apache Spark Documentation: Configuration - spark.sql.files.maxPartitionBytes
• Databricks Documentation on Data Sources: Databricks Data Sources Guide
A table is registered with the following code:
Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?
A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
B. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
C. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
Explanation:
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
References:
• Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A. If task A fails during a scheduled run, which statement describes the results of this run?
A. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
B. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are
executed in a dependency graph. If a task fails, the downstream tasks that depend on it are
skipped and marked as Upstream failed. However, the failed task may have already
committed some changes to the Lakehouse before the failure occurred, and those changes
are not rolled back automatically. Therefore, the job run may result in a partial update of the
Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to
ensure that the changes are only committed when the entire job run succeeds.
Alternatively, you can use the Run if condition to configure tasks to run even when some or
all of their dependencies have failed, allowing your job to recover from failures and
continue running. References:
transactional writes: https://docs.databricks.com/delta/deltaintro.html#transactional-writes
Run if: https://docs.databricks.com/en/workflows/jobs/conditional-tasks.html
Review the following error traceback:
Which statement describes the error being raised?
A. The code executed was PvSoark but was executed in a Scala notebook.
B. There is no column in the table named heartrateheartrateheartrate
C. There is a type error because a column object cannot be multiplied.
D. There is a type error because a DataFrame object cannot be multiplied.
E. There is a syntax error because the heartrate column is not correctly identified as a column.
Explanation:
The error being raised is an AnalysisException, which is a type of exception
that occurs when Spark SQL cannot analyze or execute a query due to some logical or
semantic error1. In this case, the error message indicates that the query cannot resolve the
column name ‘heartrateheartrateheartrate’ given the input columns ‘heartrate’ and ‘age’.
This means that there is no column in the table named ‘heartrateheartrateheartrate’, and
the query is invalid. A possible cause of this error is a typo or a copy-paste mistake in the
query. To fix this error, the query should use a valid column name that exists in the table,
such as ‘heartrate’. References: AnalysisException
A junior data engineer has been asked to develop a streaming data pipeline with a grouped
aggregation using DataFrame df. The pipeline needs to calculate the average humidity and
average temperature for each non-overlapping five-minute interval. Events are recorded
once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this
task
A. to_interval("event_time", "5 minutes").alias("time")
B. window("event_time", "5 minutes").alias("time")
C. "event_time"
D. window("event_time", "10 minutes").alias("time")
E. lag("event_time", "10 minutes").alias("time")
Explanation:
This is the correct answer because the window function is used to group
streaming data by time intervals. The window function takes two arguments: a time column
and a window duration. The window duration specifies how long each window is, and must
be a multiple of 1 second. In this case, the window duration is “5 minutes”, which means
each window will cover a non-overlapping five-minute interval. The window function also
returns a struct column with two fields: start and end, which represent the start and end
time of each window. The alias function is used to rename the struct column as “time”.
Verified References: [Databricks Certified Data Engineer Professional], under “Structured
Streaming” section; Databricks Documentation, under “WINDOW” section.
https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarkingapache-sparks-structured-streaming.html
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