Free Practice Questions for Databricks Databricks-Certified-Professional-Data-Engineer Exam

This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability. Verified References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “Database object privileges” section.

https://docs.databricks.com/en/lakehouse/data-objects.html

End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.

References:

Software Testing: End-to-End Testing

Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column ifthe table has more columns. Therefore, the junior data engineer’s suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data. References:

Optimizations: https://docs.delta.io/latest/optimizations-oss.html

Full-text search with Elasticsearch: https://docs.databricks.com/data/data-sources/elasticsearch.html

Natural language processing: https://docs.databricks.com/applications/nlp/index.html

When joining a stream of advertisement impressions with a stream of user clicks, you want to minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition thatclickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

https://delta.io/blog/2023-04-19-faster-aggregations-metadata/#:~:text=You%20can%20get%20the%20number,a%20given%20Delta%20table%20version .

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-regular-expressions

https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs

https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html

https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html

In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When thevacuumcommand is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations likevacuumon these clones.References: Databricks documentation on Delta Lake, particularly the sections on cloning tables (shallow and deep cloning) and data retention with thevacuumcommand (https://docs.databricks.com/delta/index.html ).

This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company’s data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under “Databricks Workspace” section; Databricks Documentation, under “Choose a region” section.

Delta Lake'sLiquid Clusteringis an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.

When performing writes to aLiquid Clusteredtable, some write operations automatically maintain clustering, while othersdo not.

Explanation of Each Option:

(A) spark.writestream.format('delta').mode('append') (Correct Answer)

Reason:Streaming writes (writestream) donotsupportLiquid Clusteringbecause streaming data arrives in micro-batches.

Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.

Delta Lake documentation states that Liquid Clustering is only supported for batch writes.

(B) CTAS and RTAS statements

Reason:CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS)are batch operationsand can enforce Liquid Clustering.

These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.

(C) INSERT INTO operations

Reason:INSERT INTOis supportedfor Liquid Clustering because it is a batch operation.

While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.

(D) spark.write.format('delta').mode('append')

Reason:Batch append operationsare supportedfor Liquid Clustering.

Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.

Conclusion:

Sincestreaming append operations do not support Liquid Clustering, option(A)is the correct answer.

References:

Liquid Clustering in Delta Lake - Databricks Documentation