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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?

A) Spark History Server
B) Apache Ambari
C) spark Web UI
D) Hadoop YARN ResourceManager UI

2. You're working with an ETL pipeline that extracts data from multiple sources. How can you ensure that the pipeline only processes the latest data and avoids re-processing already processed data?

A) Implement a custom mechanism to track the last processed record for each source and filter data accordingly.
B) Use timestamps or versioning information provided by the data sources to identify new data.
C) Rely on Airflow's built-in mechanisms to handle data freshness automatically.
D) Configure the data sources to only provide new data by default.

3. You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

A) Hardcode all data quality checks and data sources directly within the DAG code.
B) Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
C) Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
D) Utilize Airflow variables to store configuration details like data source paths and check thresholds.

4. Which technique in the Optimization Framework is primarily used to improve query performance by reducing the number of rows to be scanned in a table?

A) Data replication
B) Predicate pushdown
C) Indexing
D) Data compaction

5. For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

A) The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
B) Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
C) Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.
D) Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.

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