Welcome! Preparing for the Cloudera Data Engineer (CDP-3002) certification exam can be a daunting task, but we're here to make it easier for you. Here are the sample questions that will help you become familiar with the Cloudera CDP-3002 exam style and structure. We encourage you to try our Demo Cloudera Data Engineer Certification Practice Exam to measure your understanding of the exam structure in an environment that simulates the actual test environment.
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Cloudera CDP-3002 Sample Questions:
01. You want to see executor memory usage during a job. Which Spark UI tab provides this?
a) Storage
b) SQL
c) DAG
d) Executors
02. Which method removes a cached DataFrame from memory/disk?
a) unpersist()
b) clear()
c) stop()
d) drop()
03. Your Spark executors fail due to insufficient memory in Kubernetes pods. Which property should be tuned?
a) spark.sql.shuffle.partitions
b) spark.hadoop.dfs.blocksize
c) spark.executor.memory
d) spark.driver.extraJavaOptions
04. You need to add a new column email to an Iceberg table without rewriting existing data. Which statement is correct?
a) You must rebuild the entire table
b) This requires Ranger catalog integration
c) Only Hive supports altering schemas
d) Iceberg supports column addition via schema evolution
05. A data engineer creates a Cloudera Iceberg table for analytical queries. What is Iceberg primarily designed to solve compared to Hive tables?
a) Schema evolution and hidden partitioning
b) Faster Kerberos ticket renewal
c) Improved Yarn node scheduling
d) Automated replication to Ranger
06. A Spark query is taking too long. You run df.explain("formatted") and see multiple Exchange stages. What does this indicate?
a) Data locality achieved
b) Caching in memory
c) Shuffle operations between stages
d) UDF optimization applied
07. A DataFrame is used in multiple downstream aggregations. What should you do to reduce recomputation?
a) Add more shuffle partitions
b) Cache or persist the DataFrame
c) Increase HDFS replication
d) Enable speculative execution
08. A Spark SQL query on a partitioned table scans all partitions. How can you limit the scan to only required partitions?
a) Lower Kerberos ticket timeout
b) Enable speculative execution
c) Increase replication factor
d) Add partition filter in the WHERE clause
09. A Spark job writing into Hive fails with “Permission denied” error. Ranger is enabled. What’s the first step?
a) Check Ranger policy granting write access to target table/database
b) Restart Hive Metastore service
c) Lower spark.sql.shuffle.partitions
d) Enable speculative execution
10. A Spark app should run driver pods in a specific Kubernetes node pool with SSDs. Which property enables this?
a) spark.executor.memory
b) spark.kubernetes.node.selector
c) spark.sql.broadcastTimeout
d) spark.dynamicAllocation.initialExecutors
Answers:
|
Question: 1 |
Answer: d |
Question: 2 |
Answer: a |
|
Question: 3 |
Answer: c |
Question: 4 |
Answer: d |
|
Question: 5 |
Answer: a |
Question: 6 |
Answer: c |
|
Question: 7 |
Answer: b |
Question: 8 |
Answer: d |
|
Question: 9 |
Answer: a |
Question: 10 |
Answer: b |
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