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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformation, Cleansing, and Quality | 10% | - Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data |
| Topic 2: Data Modelling | 6% | - Delta Lake table design - Medallion Architecture implementation - Schema design and management |
| Topic 3: Data Ingestion & Acquisition | 7% | - Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion |
| Topic 4: Monitoring and Alerting | 10% | - Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications |
| Topic 5: 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 6: Cost & Performance Optimisation | 13% | - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling |
| Topic 7: Data Sharing and Federation | 5% | - Unity Catalog data sharing - Cross-workspace and cross-cloud access |
| Topic 8: Debugging and Deploying | 10% | - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors |
| Topic 9: Data Governance | 7% | - Data lineage and metadata tracking - Policy enforcement - Unity Catalog management |
| Topic 10: Ensuring Data Security and Compliance | 10% | - Compliance standards implementation - Access control and permissions - Data encryption and masking |
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NEW QUESTION # 19
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
Answer: C
Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.
NEW QUESTION # 20
A nightly job ingests data into a Delta Lake table using the following code:
The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
Answer: A
Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html
NEW QUESTION # 21
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: A
Explanation:
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.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks
NEW QUESTION # 22
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed." Overwriting the table nightly makes it difficult to isolate just the changed rows. With CDF enabled, if you update the table using MERGE so only changed rows are touched, you can read exactly those changed rows from CDF for the last 24 hours and score only them, which is simpler and more efficient.
Reference:
NEW QUESTION # 23
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?
Answer: C
Explanation:
* Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables."
* Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." References: Delta Lake Change Data Feed; Delta Lake MERGE INTO.
NEW QUESTION # 24
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