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100% Pass 2026 High-quality Cloudera CDP-3002: CDP Data Engineer - Certification Exam Latest Exam BookAlmost everyone is trying to get the Cloudera CDP-3002 certification to update their CV or get the desired job. Every student faces just one problem and that is not finding updated study material. Applicants are always confused about where to buy real Cloudera CDP-3002 Dumps Questions and prepare for the CDP Data Engineer - Certification Exam (CDP-3002) exam in less time. Nowadays everyone is interested in getting the CDP Data Engineer - Certification Exam (CDP-3002) certificate because it has multiple benefits for Cloudera career. Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q19-Q24):NEW QUESTION # 19
Explain the role of Spark MLIib and its functionalities in building machine learning pipelines on Spark.
A. It offers functionalities for data pre-processing, feature engineering, and model training within Spark.
B. Spark MLIib is a comprehensive library for machine learning tasks within Spark
C. It provides pre-built machine learning algorithms and tools for building and evaluating models on Spark.
D. It allows integration with external machine learning libraries like TensorFlow or scikit-learn.
Answer: B
Explanation:
Spark MLIib is a comprehensive library for machine learning tasks within Spark, offering a wide range of algorithms, tools, and functionalities. It allows data pre-processing, feature engineering, model training, and evaluation, enabling users to build and deploy machine learning pipelines on Spark.
NEW QUESTION # 20
What is the difference between RDDs and DataFrames in Spark?
A. Both B and C
B. RDDs are mutable, while DataFrames are immutable
C. RDDs represent distributed datasets, while DataFrames work with structured data
D. RDDs are lower-level abstractions, while DataFrames offer higher-level functionalities
Answer: A
Explanation:
RDDs (Resilient Distributed Datasets) are the fundamental data structure in Spark, representing distributed collections of records. DataFrames extend RDDs, providing a table-like view with schema enforcement and richer functionalities for data manipulation.
NEW QUESTION # 21
You're using Iceberg for streaming data ingestion. A critical use case requires exactly-once processing guarantees. Which considerations are most important for achieving this? (Choose two)
A. Using a data format like JSON, which is less prone to corruption than Parquet.
B. Leveraging a CDC (Change Data Captur tool integrated with Iceberg.
C. Carefully configuring the streaming source to handle retries and failures
D. Enabling Iceberg's transactional writes
E. Disabling Iceberg's snapshot isolation feature.
Answer: C,D
Explanation:
A). Iceberg transactions are key for exactly-once guarantees within the table itself.
B). The streaming source needs to cooperate to ensure no duplicate or missing data even with retries.
C). Data format has little impact on exactly-once; it's more about source reliability.
D). Snapshot isolation is desirable and unrelated to exactly-once.
E). CDC can be part of the solution but doesn't guarantee exactly-once on its own.
NEW QUESTION # 22
Consider the following code snippet:# Sample DataFrame (assuming it exists) df = spark.createDataFrame(...)
# Attempt to add a new column with a case-when expression (fix the error) df = df.withColumn("category", F.when(df["price"] ] 100, "Expensive").otherwise("Cheap")) df.show() What is the error in this code, and how can it be fixed?
A. The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.
B. The error is using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.
C. The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")
D. There is no error in the code snippet.
Answer: A
Explanation:
The error lies in attempting to modify the original DataFrame in-place. The correct approach is to use withColumn to create a new DataFrame with the added column, as shown in the fixed code:df = df.withColumn("category", ] 100, "Expensive").otherwise("Cheap"))
NEW QUESTION # 23
In the context of packaging a PySpark application, what is the purpose of the 'requirements.txt' file?
A. To list all the third-party dependencies required by the application.
B. To define the Spark version compatible with the application.
C. To list the environment variables needed for the application.
D. To specify the Python version required for the application.
Answer: A
Explanation:
The 'requirements.txt' file is used to list all third-party libraries (dependencies) that the PySpark application needs. These dependencies are then installed using pip.
NEW QUESTION # 24
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