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
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.
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.
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible. A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team. Which statement captures best practices for this situation?
A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
B. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
C. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
D. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
Explanation:
The best practice in such scenarios is to ensure that production data is
handled securely and with proper access controls. By granting only read access to
production data in development and testing environments, it mitigates the risk of
unintended data modification. Additionally, maintaining isolated databases for different
environments helps to avoid accidental impacts on production data and systems.
References:
Databricks best practices for securing data:
https://docs.databricks.com/security/index.html
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
A. Regex
B. Julia
C. pyspsark.ml.feature
D. Scala Datasets
E. C++
Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in
text. They can be used to identify key areas of text when parsing Spark Driver log4j output,
such as the log level, the timestamp, the thread name, the class name, the method name,
and the message. Regex can be applied in various languages and frameworks, such as
Scala, Python, Java, Spark SQL, and Databricks notebooks.
References:
https://docs.databricks.com/notebooks/notebooks-use.html#use-regularexpressions
https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regularexpressions-in-udfs
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records. In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?
A. Set the configuration delta.deduplicate = true.
B. VACUUM the Delta table after each batch completes.
C. Perform an insert-only merge with a matching condition on a unique key
D. Perform a full outer join on a unique key and overwrite existing data.
E. Rely on Delta Lake schema enforcement to prevent duplicate records.
Explanation:
To deduplicate data against previously processed records as it is inserted
into a Delta table, you can use the merge operation with an insert-only clause. This allows
you to insert new records that do not match any existing records based on a unique key,
while ignoring duplicate records that match existing records. For example, you can use the
following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key =
source_table.unique_key WHEN NOT MATCHED THEN INSERT *
This will insert only the records from the source table that have a unique key that is not
present in the target table, and skip the records that have a matching key. This way, you
can avoid inserting duplicate records into the Delta table.
References:
https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-usingmerge
https://docs.databricks.com/delta/delta-update.html#insert-only-merge
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 tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
A. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
B. All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
C. All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
D. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
E. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta
Lake table from an existing Parquet file stored in DBFS. The query also uses the
LOCATION keyword to specify the path to the Parquet file as
/mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query
creates an external table, which 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 from an existing directory in a cloud storage system, such as DBFS or S3, that
contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be
created in the storage container mounted to /mnt/finance_eda_bucket with the new name
prod.sales_by_store. The command will not change any data or move any files in the
storage container; it will only update the table reference in the metastore and create a new
Delta transaction log for the renamed table.
Verified References: [Databricks Certified Data
Engineer Professional], under “Delta Lake” section; Databricks Documentation, under
“ALTER TABLE RENAME TO” section; Databricks Documentation, under “Create an
external table” section.
Which statement describes the default execution mode for Databricks Auto Loader?
A. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
C. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
D. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
Explanation:
Databricks Auto Loader simplifies and automates the process of loading data
into Delta Lake. The default execution mode of the Auto Loader identifies new files by
listing the input directory. It incrementally and idempotently loads these new files into the
target Delta Lake table. This approach ensures that files are not missed and are processed
exactly once, avoiding data duplication. The other options describe different mechanisms
or integrations that are not part of the default behavior of the Auto Loader.
References:
Databricks Auto Loader Documentation: Auto Loader Guide
Delta Lake and Auto Loader: Delta Lake Integration
Which statement characterizes the general programming model used by Spark Structured Streaming?
A. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
C. Structured Streaming uses specialized hardware and I/O streams to achieve subsecond latency for data transfer.
D. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
Explanation:
This is the correct answer because it characterizes the general programming
model used by Spark Structured Streaming, which is to treat a live data stream as a table
that is being continuously appended. This leads to a new stream processing model that is
very similar to a batch processing model, where users can express their streaming
computation using the same Dataset/DataFrame API as they would use for static data. The
Spark SQL engine will take care of running the streaming query incrementally and
continuously and updating the final result as streaming data continues to arrive.
Verified
References: [Databricks Certified Data Engineer Professional], under “Structured
Streaming” section; Databricks Documentation, under “Overview” section.
A Delta Lake table was created with the below query: Realizing that the original query had a typographical error, the below code was executed: ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store Which result will occur after running the second command?
A. The table reference in the metastore is updated and no data is changed.
B. The table name change is recorded in the Delta transaction log.
C. All related files and metadata are dropped and recreated in a single ACID transaction.
D. The table reference in the metastore is updated and all data files are moved.
E. A new Delta transaction log Is created for the renamed table.
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta
Lake table from an existing Parquet file stored in DBFS. The query also uses the
LOCATION keyword to specify the path to the Parquet file as
/mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query
creates an external table, which 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 from an existing directory in a cloud storage system, such as DBFS or S3, that
contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in
the metastore is updated and no data is changed. The metastore is a service that stores
metadata about tables, such as their schema, location, properties, and partitions. The
metastore allows users to access tables using SQL commands or Spark APIs without
knowing their physical location or format. When renaming an external table using the
ALTER TABLE RENAME TO command, only the table reference in the metastore is
updated with the new name; no data files or directories are moved or changed in the
storage system. The table will still point to the same location and use the same format as
before. However, if renaming a managed table, which is a table whose metadata and data
are both managed by Databricks, both the table reference in the metastore and the data
files in the default warehouse directory are moved and renamed accordingly.
Verified
References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section;
Databricks Documentation, under “ALTER TABLE RENAME TO” section; Databricks
Documentation, under “Metastore” section; Databricks Documentation, under “Managed
and external tables” section.
Which statement describes the correct use of pyspark.sql.functions.broadcast?
A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
C. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
Explanation:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadca
st.html
The broadcast function in PySpark is used in the context of joins. When you mark a
DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that
it can be joined with another DataFrame without shuffling the larger DataFrame across the
nodes. This is particularly beneficial when the DataFrame is small enough to fit into the
memory of each node. It helps to optimize the join process by reducing the amount of data
that needs to be shuffled across the cluster, which can be a very expensive operation in
terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a
DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this
hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to
each executor only once and joined with the larger DataFrame on each executor. This can
significantly reduce the amount of data shuffled across the network and can improve the
performance of the join operation.
In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a
specific column or a cached version on attached storage. This function is particularly useful
when one of the DataFrames in a join operation is much smaller than the other, and can fit
comfortably in the memory of each executor node.
References:
Databricks Documentation on Broadcast Joins: Databricks Broadcast Join Guide
PySpark API Reference: pyspark.sql.functions.broadcast
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