Question # 1
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.
|
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.
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
Question # 2
| 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%
|
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.
Question # 3
| 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, builtin 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. |
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.
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
Question # 4
| Which of the following is true of Delta Lake and the Lakehouse? | | A. Because Parquet compresses data row by row. strings will only be compressed when a
character is repeated multiple times. | | B. Delta Lake automatically collects statistics on the first 32 columns of each table which
are leveraged in data skipping based on query filters. | | C. Views in the Lakehouse maintain a valid cache of the most recent versions of source
tables at all times. | | D. Primary and foreign key constraints can be leveraged to ensure duplicate values are
never entered into a dimension table. | | E. Z-order can only be applied to numeric values stored in Delta Lake tables |
B. Delta Lake automatically collects statistics on the first 32 columns of each table which
are leveraged in data skipping based on query filters.
Explanation:
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are
leveraged in data skipping based on query filters1. Data skipping is a performance
optimization technique that aims to avoid reading irrelevant data from the storage
layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta
Lake can efficiently prune unnecessary files or partitions from the query plan1. This can
significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for
better compression ratios, especially for repeated or similar values within a
column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions
of source tables at all times3. Views are logical constructs that are defined by a
SQL query on one or more base tables3. Views are not materialized by default,
which means they do not store any data, but only the query definition3. Therefore,
views always reflect the latest state of the source tables when queried3. However,
views can be cached manually using the CACHE TABLE or CREATE TABLE AS
SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate
values are never entered into a dimension table. Delta Lake does not support
enforcing primary and foreign key constraints on tables. Constraints are logical
rules that define the integrity and validity of the data in a table. Delta Lake relies on
the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric
values. Z-order is a technique to optimize the layout of the data files by sorting
them on one or more columns. Z-order can improve the query performance by
clustering related values together and enabling more efficient data skipping. Zorder can be applied to any column that has a defined ordering, such as numeric,
string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]
Question # 5
| A Structured Streaming job deployed to production has been experiencing delays during
peak hours of the day. At present, during normal execution, each microbatch of data is
processed in less than 3 seconds. During peak hours of the day, execution time for each
microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming
write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less
than 10 seconds, which adjustment will meet the requirement? | | A. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle
executors to begin processing the next batch while longer running tasks from previous
batches finish. | | B. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum
execution time observed for each batch is always best practice to ensure no records are
dropped. | | C. The trigger interval cannot be modified without modifying the checkpoint directory; to
maintain the current stream state, increase the number of shuffle partitions to maximize
parallelism. | | D. Use the trigger once option and configure a Databricks job to execute the query every
10 seconds; this ensures all backlogged records are processed with each batch. | | E. Decrease the trigger interval to 5 seconds; triggering batches more frequently may
prevent records from backing up and large batches from causing spill. |
E. Decrease the trigger interval to 5 seconds; triggering batches more frequently may
prevent records from backing up and large batches from causing spill.
Explanation:
The adjustment that will meet the requirement of processing records in less
than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering
batches more frequently may prevent records from backing up and large batches from
causing spill. Spill is a phenomenon where the data in memory exceeds the available
capacity and has to be written to disk, which can slow down the processing and increase
the execution time1. By reducing the trigger interval, the streaming query can process
smaller batches of data more quickly and avoid spill. This can also improve the latency and
throughput of the streaming job2.
The other options are not correct, because:
Option A is incorrect because triggering batches more frequently does not allow
idle executors to begin processing the next batch while longer running tasks from
previous batches finish. In fact, the opposite is true. Triggering batches more
frequently may cause concurrent batches to compete for the same resources and
cause contention and backpressure2. This can degrade the performance and
stability of the streaming job.
Option B is incorrect because increasing the trigger interval to 30 seconds is not a
good practice to ensure no records are dropped. Increasing the trigger interval
means that the streaming query will process larger batches of data less frequently,
which can increase the risk of spill, memory pressure, and timeouts12. This can
also increase the latency and reduce the throughput of the streaming job.
Option C is incorrect because the trigger interval can be modified without
modifying the checkpoint directory. The checkpoint directory stores the metadata
and state of the streaming query, such as the offsets, schema, and configuration3.
Changing the trigger interval does not affect the state of the streaming query, and
does not require a new checkpoint directory. However, changing the number of
shuffle partitions may affect the state of the streaming query, and may require a
new checkpoint directory4.
Option D is incorrect because using the trigger once option and configuring a
Databricks job to execute the query every 10 seconds does not ensure that all
backlogged records are processed with each batch. The trigger once option
means that the streaming query will process all the available data in the source
and then stop5. However, this does not guarantee that the query will finish
processing within 10 seconds, especially if there are a lot of records in the source.
Moreover, configuring a Databricks job to execute the query every 10 seconds
may cause overlapping or missed batches, depending on the execution time of the
query.
References:
Memory Management Overview, Structured Streaming Performance Tuning
Guide, Checkpointing, Recovery Semantics after Changes in a Streaming Query, Triggers
Question # 6
| Which distribution does Databricks support for installing custom Python code packages? | | A. sbt | | B. CRAN | | C. CRAM | | D. nom | | E. Wheels |
D. nom
Question # 7
| Which Python variable contains a list of directories to be searched when trying to locate
required modules? | | A. importlib.resource path | | B. sys.path | | C. os-path | | D. pypi.path | | E. pylib.source |
B. sys.path
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FAQs of Databricks-Certified-Professional-Data-Engineer Exam
What
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This
exam assesses your ability to use Databricks to perform advanced data engineering tasks,
such as building pipelines, data modelling, and working with tools like Apache
Spark and Delta Lake.
Who
should take this exam?
Ideal
candidates are data engineers with at least one year of experience in relevant
areas and a strong understanding of the Databricks platform.
Is
there any required training before taking the exam?
There
are no prerequisites, but Databricks recommends relevant training to ensure
success.
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The
exam covers data ingestion, processing, analytics, and visualization using Databricks,
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The
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recommended that you be familiar with the latest versions. For the latest
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The
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While
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libraries like Koalas and MLflow can be beneficial. For notebooks and
libraries, refer to Databricks documentation: https://docs.databricks.com/en/notebooks/index.html.
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