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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Topic 2: Data Transformation, Cleansing, and Quality | 10% | - Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks |
| Topic 3: Data Sharing and Federation | 5% | - Cross-workspace and cross-cloud access - Unity Catalog data sharing |
| Topic 4: Data Ingestion & Acquisition | 7% | - Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution |
| Topic 5: Ensuring Data Security and Compliance | 10% | - Data encryption and masking - Compliance standards implementation - Access control and permissions |
| Topic 6: Developing Code for Data Processing using Python and SQL | 22% | - Integration with Databricks APIs and tools - Batch and incremental processing logic - Data transformation and aggregation |
| Topic 7: Monitoring and Alerting | 10% | - Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging |
| Topic 8: Debugging and Deploying | 10% | - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices |
| Topic 9: Data Modelling | 6% | - Schema design and management - Medallion Architecture implementation - Delta Lake table design |
| Topic 10: Cost & Performance Optimisation | 13% | - Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) |
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NEW QUESTION # 150
What is the purpose of gold layer in Multi hop architecture?
Answer: A
Explanation:
Explanation
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
NEW QUESTION # 151
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?
Answer: D
Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter1. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
The other options are not correct, because:
Option A is incorrect because spark.conf.get("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
Option D is incorrect because dbutils.notebooks.getParam("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.
NEW QUESTION # 152
Your team member is trying to set up a delta pipeline and build a second gold table to the same pipeline with aggregated metrics based on an existing Delta Live table called sales_orders_cleaned but he is facing a problem in starting the pipeline, the pipeline is failing to state it cannot find the table sales_orders_cleaned, you are asked to identify and fix the problem.
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city
Answer: C
Explanation:
Explanation
The answer is, Sales_orders_cleaned table is missing schema name LIVE
Every Delta live table should have schema LIVE
Here is the correct syntax,
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM LIVE.sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city
NEW QUESTION # 153
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?
Answer: D
Explanation:
* Exact extract: "A shallow clone creates a copy of the metadata that references the source data files; it is fast and inexpensive."
* Exact extract: "A deep clone copies the data."
References: Delta Lake cloning (shallow vs deep).
NEW QUESTION # 154
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable
Answer: A
Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html
NEW QUESTION # 155
......
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