Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

Partitioning a Delta Lake table is a strategy used to improve query performance by dividing the table into distinct segments based on the values of a specific column. This approach allows queries to scan only the relevant partitions, thereby reducing the amount of data read and enhancing performance.

Considerations for Choosing a Partition Column:

Cardinality: Columns with high cardinality (i.e., a large number of unique values) are generally poor choices for partitioning. High cardinality can lead to a large number of small partitions, which can degrade performance.

Query Patterns: The partition column should align with common query filters. If queries frequently filter data based on a particular column, partitioning by that column can be beneficial.

Partition Size: Each partition should ideally contain at least 1 GB of data. This ensures that partitions are neither too small (leading to too many partitions) nor too large (negating the benefits of partitioning).

Evaluation of Columns:

date:

Cardinality: Typically low, especially if data spans over days, months, or years.

Query Patterns: Many analytical queries filter data based on date ranges.

Partition Size: Likely to meet the 1 GB threshold per partition, depending on data volume.

user_id:

Cardinality: High, as each user has a unique ID.

Query Patterns: While some queries might filter by user_id, the high cardinality makes it unsuitable for partitioning.

Partition Size: Partitions could be too small, leading to inefficiencies.

post_id:

Cardinality: Extremely high, with each post having a unique ID.

Query Patterns: Unlikely to be used for filtering large datasets.

Partition Size: Each partition would be very small, resulting in a large number of partitions.

post_time:

Cardinality: High, especially if it includes exact timestamps.

Query Patterns: Queries might filter by time, but the high cardinality poses challenges.

Partition Size: Similar to user_id, partitions could be too small.

Conclusion:

Given the considerations, the date column is the most suitable candidate for partitioning. It has low cardinality, aligns with common query patterns, and is likely to result in appropriately sized partitions.

 This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under “Language Interoperability” section; Databricks Documentation, under “Mix languages” section.

In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.