PASS DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER GUIDE, DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER LEARNING MATERIALS

Pass Databricks-Certified-Professional-Data-Engineer Guide, Databricks-Certified-Professional-Data-Engineer Learning Materials

Pass Databricks-Certified-Professional-Data-Engineer Guide, Databricks-Certified-Professional-Data-Engineer Learning Materials

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Databricks Certified Professional Data Engineer certification exam is a challenging exam that requires candidates to demonstrate their understanding of Databricks and data engineering concepts. Databricks-Certified-Professional-Data-Engineer exam consists of multiple-choice questions, and candidates have three hours to complete the exam. Databricks-Certified-Professional-Data-Engineer Exam covers various topics, including data modeling, data warehousing, data governance, and working with Databricks clusters. To pass the exam, candidates must achieve a minimum passing score of 70%.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

  • A. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
  • B. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
  • C. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
  • D. Convert the list of configuration values to a dictionary of table settings, using table names as keys.

Answer: D

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.


NEW QUESTION # 64
A Delta Lake table representing metadata about content posts from users has the following schema:
* user_id LONG
* post_text STRING
* post_id STRING
* longitude FLOAT
* latitude FLOAT
* post_time TIMESTAMP
* date DATE
Based on the above schema, which column is a good candidate for partitioning the Delta Table?

  • A. post_id
  • B. post_time
  • C. date
  • D. user_id

Answer: C

Explanation:
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.
References:
* Delta Lake Best Practices
* Partitioning in Delta Lake


NEW QUESTION # 65
The Delta Live Table Pipeline is configured to run in Production mode using the continuous Pipe-line Mode.
what is the expected outcome after clicking Start to update the pipeline?

  • A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped
  • B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated
  • C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional testing
  • D. All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)
  • E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

Answer: D

Explanation:
Explanation
The answer is,
All datasets will be updated continuously and the pipeline will not shut down. The compute re-sources will persist with the pipeline until it is shut down since the execution mode is chosen to be continuous. It does not matter if the pipeline mode is development or production, pipeline mode only matters during the pipeline initialization.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
Development:
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
Production:
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale cre-dentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default,

pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Delta Live Tables supports two different modes of execution:
Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipe-line are updated after their dependent data sources have been updated.
Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data Please review additional DLT concepts using the below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c


NEW QUESTION # 66
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. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
  • B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
  • C. Whenever a database is being created, make sure that the location keyword is used
  • D. Whenever a table is being created, make sure that the location keyword is used.
  • E. When tables are created, make sure that the external keyword is used in the create table statement.

Answer: D

Explanation:
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.


NEW QUESTION # 67
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

  • A. Cluster: Existing All-Purpose Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1
  • B. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: 1
  • C. Cluster: New Job Cluster;
    Retries: Unlimited;
    Maximum Concurrent Runs: Unlimited
  • D. Cluster: New Job Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1
  • E. Cluster: Existing All-Purpose Cluster;
    Retries: None;
    Maximum Concurrent Runs: 1

Answer: B

Explanation:
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active.
Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html


NEW QUESTION # 68
......

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