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Cloudera CDP Data Engineer - Certification Sample Questions:

1. For iterative machine learning algorithms in Spark, which caching level minimizes the trade-off between computation time and storage efficiency?

A) DISK ONLY
B) MEMORY ONLY
C) MEMORY ONLY SER
D) MEMORY AND DISK SER

2. You are working with a large dataset consisting of multiple files. How can you efficiently load the data into Spark while considering efficient storage and processing?

A) Load each file individually using spark.read.textFile("/path/to/file")
B) Use spark.read.textFile("/path/to/directory/") to read all files at once
C) Leverage partitioning techniques like spark.read.textFile("/path/to/directory").repartition(n)
D) All of the above

3. 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 using the wrong syntax for case-when expressions. Fix: Use SQL-like syntax with CASE WHEN and END.
B) There is no error in the code snippet.
C) The error is missing parentheses around the conditions in the when function. Fix: F.when((df["price"] ] 100), "Expensive").otherwise("Cheap")
D) The error is attempting to modify the original DataFrame in-place. Fix: Use df.withColumn to create a new DataFrame with the added column.

4. You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

A) Configure the DAG to retry failed tasks automatically with a set number of retries.
B) Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.
C) Implement exception handling within your custom Python operators and define retry behavior manually.
D) All of the above

5. You are writing a PySpark application where you need to collect the final results from various Executors and present them to the user. Which aspect of the Spark Driver's role is primarily involved in this process?

A) Optimizing resource utilization within the Kubernetes cluster.
B) Collecting final results from Executors and providing the output.
C) Managing Kubernetes API interactions for Executor lifecycle.
D) Constructing the logical plan from the user application.

Solutions: