Question # 1
| Which statement describes Delta Lake Auto Compaction?
| | A. An asynchronous job runs after the write completes to detect if files could be further
compacted; if yes, an optimize job is executed toward a default of 1 GB. | | B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the
most recent job. | | C. Optimized writes use logical partitions instead of directory partitions; because partition
boundaries are only represented in metadata, fewer small files are written. | | D. Data is queued in a messaging bus instead of committing data directly to memory; all
data is committed from the messaging bus in one batch once the job is complete. | | E. An asynchronous job runs after the write completes to detect if files could be further
compacted; if yes, an optimize job is executed toward a default of 128 MB. |
E. An asynchronous job runs after the write completes to detect if files could be further
compacted; if yes, an optimize job is executed toward a default of 128 MB.
Explanation:
This is the correct answer because it describes the behavior of Delta Lake
Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake
tables by coalescing small files into larger ones. Auto Compaction runs as an
asynchronous job after a write to a table has succeeded and checks if files within a partition
can be further compacted. If yes, it runs an optimize job with a default target file size of 128
MB. Auto Compaction only compacts files that have not been compacted previously.
Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake”
section; Databricks Documentation, under “Auto Compaction for Delta Lake on Databricks”
section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on
the cluster that has performed the write. Auto compaction only compacts files that haven’t
been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size
Question # 2
| Which configuration parameter directly affects the size of a spark-partition upon ingestion
of data into Spark? | | A. spark.sql.files.maxPartitionBytes | | B. spark.sql.autoBroadcastJoinThreshold | | C. spark.sql.files.openCostInBytes | | D. spark.sql.adaptive.coalescePartitions.minPartitionNum | | E. spark.sql.adaptive.advisoryPartitionSizeInBytes |
A. spark.sql.files.maxPartitionBytes
Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a
configuration parameter that directly affects the size of a spark-partition upon ingestion of
data into Spark. This parameter configures the maximum number of bytes to pack into a
single partition when reading files from file-based sources such as Parquet, JSON and
ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in
size, unless there are too many small files or only one large file. Verified References:
[Databricks Certified Data Engineer Professional], under “Spark Configuration”
Question # 3
| A data architect has designed a system in which two Structured Streaming jobs will
concurrently write to a single bronze Delta table. Each job is subscribing to a different topic
from an Apache Kafka source, but they will write data with the same schema. To keep the
directory structure simple, a data engineer has decided to nest a checkpoint directory to be
shared by both streams.
The proposed directory structure is displayed below:
Which statement describes whether this checkpoint directory structure is valid for the given
scenario and why?
| | A. No; Delta Lake manages streaming checkpoints in the transaction log. | | B. Yes; both of the streams can share a single checkpoint directory. | | C. No; only one stream can write to a Delta Lake table. | | D. Yes; Delta Lake supports infinite concurrent writers. | | E. No; each of the streams needs to have its own checkpoint directory. |
E. No; each of the streams needs to have its own checkpoint directory.
Explanation:
This is the correct answer because checkpointing is a critical feature of
Structured Streaming that provides fault tolerance and recovery in case of failures.
Checkpointing stores the current state and progress of a streaming query in a reliable
storage system, such as DBFS or S3. Each streaming query must have its own checkpoint
directory that is unique and exclusive to that query. If two streaming queries share the
same checkpoint directory, they will interfere with each other and cause unexpected errors
or data loss. Verified References: [Databricks Certified Data Engineer Professional], under
“Structured Streaming” section; Databricks Documentation, under “Checkpointing” section.
Question # 4
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 # 5
| The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the spark UI's Storage tab to signal
that a cached table is not performing optimally?
| | A. Size on Disk is> 0 | | B. The number of Cached Partitions> the number of Spark Partitions | | C. The RDD Block Name included the '' annotation signaling failure to cache | | D. On Heap Memory Usage is within 75% of off Heap Memory usage |
C. The RDD Block Name included the '' annotation signaling failure to cache
Explanation:
In the Spark UI's Storage tab, an indicator that a cached table is not
performing optimally would be the presence of the _disk annotation in the RDD Block
Name. This annotation indicates that some partitions of the cached data have been spilled
to disk because there wasn't enough memory to hold them. This is suboptimal because
accessing data from disk is much slower than from memory. The goal of caching is to keep
data in memory for fast access, and a spill to disk means that this goal is not fully achieved.
Question # 6
| A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to
create a Type 1 table representing all of the values that have ever been valid for all rows in
a bronze table created with the property delta.enableChangeDataFeed = true. They plan to
execute the following code as a daily job:
Which statement describes the execution and results of running the above query multiple
times?
| | A. Each time the job is executed, newly updated records will be merged into the target
table, overwriting previous values with the same primary keys. | | B. Each time the job is executed, the entire available history of inserted or updated records
will be appended to the target table, resulting in many duplicate entries. | | C. Each time the job is executed, the target table will be overwritten using the entire history
of inserted or updated records, giving the desired result. | | D. Each time the job is executed, the differences between the original and current versions
are calculated; this may result in duplicate entries for some records. | | E. Each time the job is executed, only those records that have been inserted or updated
since the last execution will be appended to the target table giving the desired result. |
B. Each time the job is executed, the entire available history of inserted or updated records
will be appended to the target table, resulting in many duplicate entries.
Explanation: Reading table’s changes, captured by CDF, using spark.read means that you
are reading them as a static source. So, each time you run the query, all table’s changes
(starting from the specified startingVersion) will be read.
Question # 7
| The data architect has mandated that all tables in the Lakehouse should be configured as
external Delta Lake tables.
Which approach will ensure that this requirement is met?
| | A. Whenever a database is being created, make sure that the location keyword is used | | B. When configuring an external data warehouse for all table storage. leverage Databricks
for all ELT. | | C. Whenever a table is being created, make sure that the location keyword is used. | | D. When tables are created, make sure that the external keyword is used in the create
table statement. | | E. When the workspace is being configured, make sure that external cloud object storage
has been mounted. |
C. Whenever a table is being created, make sure that the location keyword is used.
Explanation:
This is the correct answer because it ensures that this requirement is met.
The requirement is that all tables in the Lakehouse should be configured as external Delta
Lake tables. An external table is a table that is stored outside of the default warehouse
directory and whose metadata is not managed by Databricks. An external table can be
created by using the location keyword to specify the path to an existing directory in a cloud
storage system, such as DBFS or S3. By creating external tables, the data engineering
team can avoid losing data if they drop or overwrite the table, as well as leverage existing
data without moving or copying it. Verified References: [Databricks Certified Data Engineer
Professional], under “Delta Lake” section; Databricks Documentation, under “Create an
external table” section.
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