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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling and Storage | 20% | - Storage Optimization - Data Modeling - File Formats |
| Topic 2: Monitoring and Troubleshooting | 16% | - Performance Optimization - Troubleshooting - Monitoring |
| Topic 3: Databricks Lakehouse Platform | 24% | - Lakehouse Architecture - Delta Lake - Unity Catalog - Data Management |
| Topic 4: Data Quality and Governance | 12% | - Governance - Data Lineage - Data Quality |
| Topic 5: Data Processing | 28% | - Data Transformation - Spark SQL - Structured Streaming - ETL Pipelines |
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NEW QUESTION # 206
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?
Answer: A
Explanation:
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.
NEW QUESTION # 207
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?
Answer: A
Explanation:
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).
NEW QUESTION # 208
A data engineer is evaluating tools to build a production-grade data pipeline. The team must process change data from cloud object storage, filter out or isolate invalid records, and ensure the timely delivery of clean data to downstream consumers. The team is small, under tight deadlines, and wants to minimize operational overhead while keeping pipelines auditable and maintainable.
Which approach should the data engineer implement?
Answer: D
Explanation:
LDP provides a declarative framework for building production-grade pipelines with minimal operational overhead. Streaming Tables and Materialized Views handle incremental processing automatically, while built-in data expectations allow invalid records to be filtered or isolated in a consistent and auditable way. This approach is well suited for small teams under tight deadlines, as it simplifies maintenance, improves reliability, and ensures timely delivery of clean data to downstream consumers.
NEW QUESTION # 209
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?
Answer: A
Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.
NEW QUESTION # 210
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
Answer: D
Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.
NEW QUESTION # 211
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