Databricks Certification Databricks-Certified-Data-Engineer-Professional

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Databricks Certified Data Engineer Professional Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen:

1. A data engineering team is setting up a Git project to automate integration tests using Databricks Asset Bundles and the Git provider's CI/CD functionalities. When a pull containing changes to their pipleline is sent, they need to run a Job to test their data pipeline. What is the correct databricks bundle command sequence to be executed from the Git provider's CI/CD automation for this task?

A) validate, deploy, run
B) init, deploy, run, validate
C) deploy, run, validate
D) init, validate, deploy, run

2. Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

A) PARTITION BY
B) BUCKETING
C) ANALYZE
D) OPTIMIZE
E) COMPACT

3. 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
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
B) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
C) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
D) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
E) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

4. A data engineer is working on a Databricks notebook that requires several third-party Python libraries. Some of these are available on PyPI, while others are custom-developed and stored as local.wheel (.whl) and source (.tar.gz) files in an S3 bucket. The goal is to ensure all dependencies are installed and correctly available across multiple jobs running on any automated cluster in a Unity Catalog-enabled workspace. The engineer needs to install the required dependencies in a way that ensures a consistent environment setup across interactive notebooks and jobs and complies with workspace security policies (no internet access). Which approach should the engineer use to install and manage these dependencies while also ensuring reproducibility and compliance?

A) Create a Python wheel file for the entire project, upload it to the Databricks Workspace Files or Volumes, and install it using a Cluster Library or pip install in a requirements.txt declared within a Databricks Asset Bundle.
B) Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
C) Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.
D) Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.

5. What statement is true regarding the retention of job run history?

A) It is retained for 60 days, during which you can export notebook run results to HTML
B) It is retained for 90 days or until the run-id is re-used through custom run configuration
C) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
D) It is retained for 60 days, after which logs are archived
E) It is retained until you export or delete job run logs

Fragen und Antworten: