Free Oracle 1Z0-1089-21 Exam Practice Questions & Explanations
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An HPC cluster of 200 compute nodes needs a POSIX-compliant shared file system for scratch data. The workload requires very high aggregate throughput and low latency, and the team is comfortable managing the file system software themselves on OCI compute.
Which two statements are correct about shared storage choices for this design? (Choose two.)
FSS is the managed NFS option in OCI, mountable by many clients, but its performance scales with stored capacity and it may not deliver the extreme parallel throughput HPC scratch needs. For that, customers commonly self-manage parallel file systems such as BeeGFS, Lustre, GlusterFS or IBM Spectrum Scale on OCI compute using local NVMe or block volumes. Object Storage (C) is not a native POSIX file system. Multi-attach block volumes require cluster-aware file system software and do not automatically create a shared namespace across 200 nodes (D). Boot volume backups (E) are not shared storage.
A retail analytics group runs occasional, unpredictable Apache Spark batch jobs that read Parquet files from Object Storage. They do not want to size, patch, or keep any Spark cluster running between jobs, and they need per-run logs and Spark UI access for troubleshooting.
Which OCI service best matches this requirement?
Data Flow is OCI's fully managed, serverless Apache Spark service: you submit an application, OCI provisions the Spark driver/executors only for the duration of the run, and it retains run logs and the Spark UI (Spark History Server) for each run. Big Data Service (B) provides long-running Hadoop/Spark clusters that must be managed and sized. Streaming (C) is for event ingest. Data Integration (D) is an ETL/dataflow design service and does not fit the described raw Spark batch job pattern.
A data science team wants to train deep learning models on OCI Data Science. They need GPU acceleration for training, want to track model versions and dependencies, and require the models to eventually be deployed as HTTP endpoints without managing servers.
Which combination of OCI Data Science features should they use?
OCI Data Science provides managed notebook sessions that can be launched on GPU shapes, a model catalog for storing and versioning model artifacts along with conda environment/dependency metadata, and model deployment, which serves a cataloged model as a managed HTTP endpoint. The other options require the team to build and operate infrastructure themselves, which contradicts the stated serverless deployment requirement, and Data Flow is a managed Spark service for data processing, not deep learning training with model serving.
A media rendering company has bursty demand. Their render farm must automatically grow when CPU utilization stays above 80 percent for several minutes and shrink again when it drops below 20 percent, while keeping cost under control.
Which approach best meets these requirements on OCI?
OCI autoscaling operates on instance pools created from an instance configuration, and metric-based (dynamic) autoscaling uses OCI Monitoring metrics such as CPU utilization with defined thresholds and cooldown periods. Manual scaling (B) is not dynamic. Cluster networks (C) are fixed-size RDMA clusters and OCI does not support live vertical scaling in that manner. Load balancer flexible shapes scale bandwidth only, not compute instances (D).
An engineering firm is migrating a tightly coupled CFD simulation to OCI. The application uses MPI across 64 bare metal BM.Optimized3.36 nodes and is extremely sensitive to inter-node latency and jitter. The architect must guarantee the lowest possible network latency between the nodes.
Which OCI capability should be used when provisioning the cluster?
Cluster networks in OCI provision instances from an HPC or GPU shape within a single availability/fault domain on a high-performance RDMA (RoCE) fabric, delivering microsecond-level latency required by tightly coupled MPI workloads. Instance pools spread across ADs (B) deliberately increase latency and are for loosely coupled, scale-out HA workloads. Extra VNICs (C) do not reduce latency for MPI. FastConnect (D) connects on-premises networks to OCI and is irrelevant to node-to-node traffic inside a cluster.