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Databricks Certified Data Engineer Professional Practice Test

DCDEP test Format | Course Contents | Course Outline | test Syllabus | test Objectives

Exam Code: DCDEP
Exam Name: Databricks Certified Data Engineer Professional
Type: Proctored certification
Total number of questions: 60
Time limit: 120 minutes
Question types: Multiple choice

Section 1: Databricks Tooling
- Explain how Delta Lake uses the transaction log and cloud object storage to ensure atomicity and durability
- Describe how Delta Lake’s Optimistic Concurrency Control provides isolation, and which transactions might conflict
- Describe basic functionality of Delta clone.
- Apply common Delta Lake indexing optimizations including partitioning, zorder, bloom filters, and file sizes
- Implement Delta tables optimized for Databricks SQL service
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)

Section 2: Data Processing (Batch processing, Incremental processing, and Optimization)
- Describe and distinguish partition hints: coalesce, repartition, repartition by range, and rebalance
- Contrast different strategies for partitioning data (e.g. identify proper partitioning columns to use)
- Articulate how to write Pyspark dataframes to disk while manually controlling the size of individual part-files.
- Articulate multiple strategies for updating 1+ records in a spark table (Type 1)
- Implement common design patterns unlocked by Structured Streaming and Delta Lake.
- Explore and tune state information using stream-static joins and Delta Lake
- Implement stream-static joins
- Implement necessary logic for deduplication using Spark Structured Streaming
- Enable CDF on Delta Lake tables and re-design data processing steps to process CDC output instead of incremental feed from normal Structured Streaming read
- Leverage CDF to easily propagate deletes
- Demonstrate how proper partitioning of data allows for simple archiving or deletion of data
- Articulate, how “smalls” (tiny files, scanning overhead, over partitioning, etc) induce performance problems into Spark queries

Section 3: Data Modeling
- Describe the objective of data transformations during promotion from bronze to silver
- Discuss how Change Data Feed (CDF) addresses past difficulties propagating updates and deletes within Lakehouse architecture
- Apply Delta Lake clone to learn how shallow and deep clone interact with source/target tables.
- Design a multiplex bronze table to avoid common pitfalls when trying to productionalize streaming workloads.
- Implement best practices when streaming data from multiplex bronze tables.
- Apply incremental processing, quality enforcement, and deduplication to process data from bronze to silver
- Make informed decisions about how to enforce data quality based on strengths and limitations of various approaches in Delta Lake
- Implement tables avoiding issues caused by lack of foreign key constraints
- Add constraints to Delta Lake tables to prevent bad data from being written
- Implement lookup tables and describe the trade-offs for normalized data models
- Diagram architectures and operations necessary to implement various Slowly Changing Dimension tables using Delta Lake with streaming and batch workloads.
- Implement SCD Type 0, 1, and 2 tables

Section 4: Security & Governance
- Create Dynamic views to perform data masking
- Use dynamic views to control access to rows and columns

Section 5: Monitoring & Logging
- Describe the elements in the Spark UI to aid in performance analysis, application debugging, and tuning of Spark applications.
- Inspect event timelines and metrics for stages and jobs performed on a cluster
- Draw conclusions from information presented in the Spark UI, Ganglia UI, and the Cluster UI to assess performance problems and debug failing applications.
- Design systems that control for cost and latency SLAs for production streaming jobs.
- Deploy and monitor streaming and batch jobs

Section 6: Testing & Deployment
- Adapt a notebook dependency pattern to use Python file dependencies
- Adapt Python code maintained as Wheels to direct imports using relative paths
- Repair and rerun failed jobs
- Create Jobs based on common use cases and patterns
- Create a multi-task job with multiple dependencies
- Design systems that control for cost and latency SLAs for production streaming jobs.
- Configure the Databricks CLI and execute basic commands to interact with the workspace and clusters.
- Execute commands from the CLI to deploy and monitor Databricks jobs.
- Use REST API to clone a job, trigger a run, and export the run output

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Question: 21
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?
1. Triggered, Incremental
2. Once, Continuous
3. Triggered, Continuous
4. Once, Incremental
5. Continuous, Incremental
Answer: C
Explanation:
The answer is Triggered, Continuous
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-concepts#-- continuous-and-triggered-pipelines
� 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 pipeline 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.
Question: 22
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?
1. Merge when code is committed
2. Pull request and review process
3. Trigger Databricks Repos API to pull the latest version of code into production folder
4. Resolve merge conflicts
5. Delete a branch
Answer: C
Explanation:
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure DevOps
Question: 23
Identify one of the below statements that can query a delta table in PySpark Dataframe API
1. Spark.read.mode("delta").table("table_name")
2. Spark.read.table.delta("table_name")
3. Spark.read.table("table_name")
4. Spark.read.format("delta").LoadTableAs("table_name")
5. Spark.read.format("delta").TableAs("table_name")
Answer: C Question: 24
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?
1. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
2. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.
3. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
4. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
5. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused. (Correct)
Answer: E Explanation: VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data retention for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can Strengthen the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.
Question: 25
Which of the following statements are correct on how Delta Lake implements a lake house?
1. Delta lake uses a proprietary format to write data, optimized for cloud storage
2. Using Apache Hadoop on cloud object storage
3. Delta lake always stores meta data in memory vs storage
4. Delta lake uses open source, open format, optimized cloud storage and scalable meta data
5. Delta lake stores data and meta data in computes memory
Answer: D
Explanation:
Delta lake is
� Open source
� Builds up on standard data format � Optimized for cloud object storage
� Built for scalable metadata handling Delta lake is not � Proprietary technology
� Storage format � Storage medium
� Database service or data warehouse
Question: 26
What are the different ways you can schedule a job in Databricks workspace?
1. Continuous, Incremental
2. On-Demand runs, File notification from Cloud object storage
3. Cron, On Demand runs
4. Cron, File notification from Cloud object storage
5. Once, Continuous
Answer: C
Explanation:
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax
Question: 27
Which of the following type of tasks cannot setup through a job?
1. Notebook
2. DELTA LIVE PIPELINE
3. Spark Submit
4. Python
5. Databricks SQL Dashboard refresh
Answer: E Question: 28
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the Databricks Lakehouse Platform?
1. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
2. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
3. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
4. Databricks Repos can store the single-source-of-truth Git repository
5. Databricks Repos can commit or push code changes to trigger a CI/CD process
Answer: E
Explanation:
Answer is Databricks Repos can commit or push code changes to trigger a CI/CD process See below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram
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