EXAM DATABRICKS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER QUESTIONS PDF | DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VALID EXAMCOLLECTION

Exam Databricks Databricks-Certified-Professional-Data-Engineer Questions Pdf | Databricks-Certified-Professional-Data-Engineer Valid Examcollection

Exam Databricks Databricks-Certified-Professional-Data-Engineer Questions Pdf | Databricks-Certified-Professional-Data-Engineer Valid Examcollection

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Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam is designed for data professionals who want to validate their skills and knowledge in building and deploying data engineering solutions using Databricks. Databricks is a unified data analytics platform that provides a collaborative environment for data engineers, data scientists, and business analysts to work together on big data projects. Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics such as data ingestion, data processing, data transformation, and data storage using Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
You are currently working on a project that requires the use of SQL and Python in a given note-book, what would be your approach

  • A. A single notebook can support multiple languages, use the magic command to switch between the two.
  • B. Use job cluster to run python and SQL Endpoint for SQL
  • C. Create two separate notebooks, one for SQL and the second for Python
  • D. Use an All-purpose cluster for python, SQL endpoint for SQL

Answer: A

Explanation:
Explanation
The answer is, A single notebook can support multiple languages, use the magic command to switch between the two.
Use %sql and %python magic commands within the same notebook


NEW QUESTION # 21
A table in the Lakehouse namedcustomer_churn_paramsis 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.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

  • A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
  • B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
  • C. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
  • D. Modify the overwrite logic to include a field populated by calling spark.sql.functions.
    current_timestamp() as data are being written; use this field to identify records written on a particular date.
  • E. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Answer: C

Explanation:
The approach that would simplify the identification of the changed records is to replace the current overwrite logic with a merge statement to modify only those records that have changed, and write logic to make predictions on the changed records identified by the change data feed. This approach leverages the Delta Lake features of merge and change data feed, which are designed to handle upserts and track row-level changes in a Delta table12. By using merge, the data engineering team can avoid overwriting the entire table every night, and only update or insert the records that have changed in the source data. By using change data feed, the ML team can easily access the change events that have occurred in the customer_churn_params table, and filter them by operation type (update or insert) and timestamp. This way, they can only make predictions on the records that have changed in the past 24 hours, and avoid re-processing the unchanged records.
The other options are not as simple or efficient as the proposed approach, because:
* Option A would require applying the churn model to all rows in the customer_churn_params table, which would be wasteful and redundant. It would also require implementing logic to perform an upsert into the predictions table, which would be more complex than using the merge statement.
* Option B would require converting the batch job to a Structured Streaming job, which would involve changing the data ingestion and processing logic. It would also require using the complete output mode, which would output the entire result table every time there is a change in the source data, which would be inefficient and costly.
* Option C would require calculating the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers, which would be computationally expensive and prone to errors. It would also require storing and accessing the previous predictions, which would add extra storage and I/O costs.
* Option D would require modifying the overwrite logic to include a field populated by calling spark.sql.
functions.current_timestamp() as data are being written, which would add extra complexity and overhead to the data engineering job. It would also require using this field to identify records written on a particular date, which would be less accurate and reliable than using the change data feed.
References: Merge, Change data feed


NEW QUESTION # 22
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table -
Which of the following describes how results are generated each time the dashboard is updated?

  • A. The total count of rows will be returned from cached results unless REFRESH is run
  • B. The total count of records is calculated from the parquet file metadata
  • C. The total count of records is calculated from the Delta transaction logs
  • D. The total count of rows is calculated by scanning all data files
  • E. The total count of records is calculated from the Hive metastore

Answer: C

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


NEW QUESTION # 23
Which of the following is true of Delta Lake and the Lakehouse?

  • A. Z-order can only be applied to numeric values stored in Delta Lake tables
  • B. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
  • 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. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

Answer: E

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. Z-order 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]


NEW QUESTION # 24
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

  • A. The Hive metastore is scanned for min and max statistics for the latitude column
  • B. The Parquet file footers are scanned for min and max statistics for the latitude column
  • C. All records are cached to an operational database and then the filter is applied
  • D. The Delta log is scanned for min and max statistics for the latitude column
  • E. All records are cached to attached storage and then the filter is applied

Answer: D

Explanation:
Explanation
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.


NEW QUESTION # 25
......

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