Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

• A. - Total VMs: 4
  - 100 GB per Executor
  - 40 Cores / Executor
• B. - Total VMs: 16
  - 25 GB per Executor
  - 10 Cores / Executor
• C. - Total VMs: 8
  - 50 GB per Executor
  - 20 Cores / Executor
• D. - Total VMs: 2
  - 200 GB per Executor
  - 80 Cores / Executor
• E. - Total VMs: 1
  - 400 GB per Executor
  - 160 Cores/Executor

A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths. How should the team use databricks.yml to satisfy these requirements?

• A. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
• B. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
• C. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
• D. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default:true and override workspace paths or artifacts under that target.

An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

• A. input_dict = input()
  date= input_dict["date"]
• B. date = spark.conf.get("date")
• C. import sys
  date = sys.argv[1]
• D. date = dbutils.notebooks.getParam("date")
• E. dbutils.widgets.text("date", "null")
  date = dbutils.widgets.get("date")

A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

• A. COPY INTO SQL command with parameters to track processed files
• B. Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
• C. Auto Loader with schema inference and evolution enabled
• D. Traditional batch processing with scheduled Databricks Jobs

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

• A. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
• B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
• C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
• D. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
• E. Both commands will fail. No new variables, tables, or views will be created.