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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Objectives
Delta Lake and Data Management	- Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning
Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Production Pipelines and Orchestration	- Databricks Workflows - Job scheduling and monitoring - Error handling and recovery strategies
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q47-Q52):

NEW QUESTION # 47
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.

Answer: E

Explanation:
The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html

NEW QUESTION # 48
Which statement describes integration testing?

A. Requires manual intervention
B. Requires an automated testing framework
C. Validates interactions between subsystems of your application
D. Validates behavior of individual elements of your application
E. Validates an application use case

Answer: C

Explanation:
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.

NEW QUESTION # 49
The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members have full permissions on the prod database and can also assign permissions to other users or groups.
B. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.
C. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.
D. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.
E. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

Answer: D

Explanation:
The GRANT USAGE ON DATABASE prod TO eng command grants the eng group the permission to use the prod database, which means they can list and access the tables and views in the database. The GRANT SELECT ON DATABASE prod TO eng command grants the eng group the permission to select data from the tables and views in the prod database, which means they can query the data using SQL or DataFrame API. However, these commands do not grant the eng group any other permissions, such as creating, modifying, or deleting tables and views, or defining custom functions. Therefore, the eng group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

NEW QUESTION # 50
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. Use %pip install in every notebook and job to install packages directly from PyPl and custom S3 paths.
B. Use an init script on the cluster to install all dependencies using pip, referencing the local file system.
C. 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.
D. Install all dependencies manually in the driver node of an interactive cluster, then export the environment and reimport on job clusters using %conda.

Answer: C

Explanation:
Packaging dependencies as wheel files and managing them through Databricks-supported artifacts ensures consistent, reproducible environments across interactive and job clusters.
Storing the wheels in Workspace Files or Unity Catalog Volumes complies with no-internet security constraints, while installing them via cluster libraries or Asset Bundles standardizes dependency management across all jobs and pipelines.

NEW QUESTION # 51
A platform engineer needs to report the resource consumption, categorized by SKU tier, across all workspaces. The engineer decides to use the system.billing.usage system table to create a query. Which SQL query will accurately return the daily usage by product?

Answer: C

Explanation:
This query correctly aggregates usage at a daily granularity by truncating the usage start timestamp to the day and summing the usage quantity, which represents DBUs. Grouping by both the derived daily value and the SKU name ensures usage is accurately categorized by product tier across all workspaces.

NEW QUESTION # 52
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