FULL FILL YOUR GOALS BY ACHIEVE THE DATABRICKS DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL CERTIFICATION

Full fill Your Goals by Achieve the Databricks Databricks-Certified-Data-Engineer-Professional Certification

Full fill Your Goals by Achieve the Databricks Databricks-Certified-Data-Engineer-Professional Certification

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

  • A. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
  • B. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.
  • C. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
  • D. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
  • E. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

Answer: B

Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column.


NEW QUESTION # 90
The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

  • A. df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
  • B. df.select("customer_id", model(*columns).alias("predictions"))
  • C. df.apply(model, columns).select("customer_id, predictions")
  • D. model.predict(df, columns)
  • E. df.map(lambda x:model(x[columns])).select("customer_id, predictions")

Answer: B

Explanation:
This code block applies the Spark UDF created from the MLflow model to the DataFrame df by selecting the existing customer_id column and the new column produced by the model, which is aliased to predictions. The model(*columns) part is where the UDF is applied to the columns specified in the columns list, and alias("predictions") is used to name the output column of the model's predictions. This will result in a DataFrame with the desired schema: "customer_id LONG, predictions DOUBLE".


NEW QUESTION # 91
A table in the Lakehouse named customer_churn_params 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.
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. 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.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. 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.

Answer: A

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 table. 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.


NEW QUESTION # 92
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 task A fails during a scheduled run, which statement describes the results of this run?

  • A. Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
  • B. Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
  • C. Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
  • D. Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
  • E. Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

Answer: A

Explanation:
When a Databricks job runs multiple tasks with dependencies, the tasks are executed in a dependency graph. If a task fails, the downstream tasks that depend on it are skipped and marked as Upstream failed. However, the failed task may have already committed some changes to the Lakehouse before the failure occurred, and those changes are not rolled back automatically. Therefore, the job run may result in a partial update of the Lakehouse. To avoid this, you can use the transactional writes feature of Delta Lake to ensure that the changes are only committed when the entire job run succeeds. Alternatively, you can use the Run if condition to configure tasks to run even when some or all of their dependencies have failed, allowing your job to recover from failures and continue running.


NEW QUESTION # 93
Which statement describes Delta Lake optimized writes?

  • A. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
  • B. Optimized writes logical partitions instead of directory partitions partition boundaries are only Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from represented in metadata fewer small files are written.
  • C. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
  • D. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

Answer: C

Explanation:
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table.
The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.


NEW QUESTION # 94
......

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