Free Oracle 1Z0-928 Exam Practice Questions & Explanations
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A retail company needs to run iterative machine learning algorithms on clickstream data that must be reused across multiple computation steps. Their current MapReduce-based solution is too slow because it reads and writes to disk at every stage. They want a framework that can cache datasets in memory across iterations to significantly speed up processing. Which Apache framework should they migrate to?
Apache Spark uses in-memory RDDs (Resilient Distributed Datasets) that can be cached across iterations, making it ideal for iterative machine learning workloads, unlike MapReduce which writes to disk between stages. Sqoop is used for transferring data between Hadoop and relational databases. Flume is used for ingesting streaming log data. Oozie is a workflow scheduler for Hadoop jobs, not a processing engine.
A company wants a fully managed Oracle Cloud service that provisions Hadoop and Spark clusters on demand, integrates with Oracle Object Storage for durable data storage, and allows them to scale compute nodes up or down based on workload without managing the underlying infrastructure. Which Oracle solution best fits this requirement?
Oracle Big Data Cloud Service (BDCS) provides fully managed, elastic Hadoop and Spark clusters in Oracle Cloud, with integration to Oracle Object Storage and the ability to scale nodes on demand. Oracle Data Integrator is an ETL/data integration tool, not a cluster provisioning service. Autonomous Transaction Processing is a managed relational database service, not a big data cluster platform. SQL Developer is a client tool for database development, unrelated to Hadoop/Spark cluster provisioning.
An organization stores a 10 GB log file in HDFS on a cluster configured with the default replication factor of 3. A disk failure occurs on one of the DataNodes holding a block of this file. Because HDFS automatically maintains multiple replicas of each block across different DataNodes, the cluster can continue serving the file without data loss, and HDFS will re-replicate the affected blocks to restore the desired replication level.
This is true. HDFS splits files into blocks and replicates each block (by default three times) across different DataNodes. If a DataNode fails, the NameNode detects the under-replicated blocks via heartbeat monitoring and instructs other DataNodes to create new replicas, restoring fault tolerance without data loss to the client.
A cluster administrator is troubleshooting a Big Data Cloud Service cluster where multiple applications (a Spark job and a Hive query) need to share cluster compute resources simultaneously without one starving the other. Which component is responsible for allocating and managing these resources across the applications?
YARN's ResourceManager negotiates and allocates cluster resources (CPU, memory) across competing applications like Spark and Hive, which is exactly its purpose as a resource negotiation layer. The NameNode manages HDFS metadata, not compute resources. ODI is an ETL tool. ZooKeeper provides coordination services, not resource allocation. The Spark Driver manages a single Spark application's execution, not cluster-wide resource sharing.
A data engineering team is processing a large dataset stored in HDFS using a distributed processing framework. They notice that the framework requires each intermediate stage to write results back to disk before the next stage can begin, resulting in slower performance compared to their newer Spark-based pipeline. Which processing framework is being described?
MapReduce writes intermediate results to disk between the map and reduce phases, which introduces significant I/O overhead compared to Spark's in-memory processing model. YARN is the resource manager, not a processing framework itself. Spark keeps data in memory across stages using RDDs, making it faster. Kafka is a messaging/streaming system, not a batch processing framework.