1Z0-1072-25 exam dumps: 318 free OCI 2025 Architect Associate practice questions

Correct answer: D

• A. Incorrect.

  Incorrect: While a larger shape might temporarily provide more local NVMe capacity, ephemeral storage is not persistent. If an instance fails or is retired, data is lost. This does not address long-term retention with minimal data loss risk.

• B. Incorrect.

  Incorrect: Auto-scaling can handle additional load by adding more compute instances, but it does not inherently solve the log retention issue. Logs stored on ephemeral local storage remain vulnerable to data loss when instances are scaled down or fail.

• C. Incorrect.

  Incorrect: Attaching a Block Volume can provide persistent storage, but merely uploading logs occasionally still carries a risk of data loss if the instance fails before the upload. Additionally, continuously writing large log volumes to a Block Volume can become costly and complex for long-term compliance.

• D. Correct.

  Correct: Regularly rotating logs to OCI Object Storage ensures durability and long-term retention. Object Storage is designed for high durability and scalability, making it a best practice for storing logs that need to be retained for extended periods.

Correct answer: A

• A. Correct.

  Correct. Creating an Instance Configuration, then using it with an Instance Pool that has autoscaling rules based on CPU metrics is the recommended method to handle fluctuating demand. This approach automatically adds or removes instances and ensures minimal downtime.

• B. Incorrect.

  Incorrect. Manually changing a single bare metal instance shape is cumbersome, can cause downtime, and does not provide automatic scaling. It also doesn't align with OCI best practices for elasticity.

• C. Incorrect.

  Incorrect. While OCI Resource Manager can automate infrastructure deployments, manually provisioning or decommissioning instances whenever demand changes is inefficient and prone to human error. It doesn't provide real-time autoscaling based on utilization metrics.

• D. Incorrect.

  Incorrect. Autoscaling block volumes only adjusts storage capacity, not CPU or memory resources. It won�t help with automatically adjusting the compute fleet when CPU usage spikes.

Correct answers: A, D

• A. Correct.

  Correct. Ephemeral local NVMe provides ultra-fast local storage but does not persist data if the instance is stopped or deleted. Attaching a persistent block volume and periodically copying your important data ensures it is retained independently of the underlying ephemeral storage.

• B. Incorrect.

  Incorrect. Ephemeral local NVMe data is not replicated automatically by OCI, and there is no built-in feature to mirror ephemeral data across Availability Domains.

• C. Incorrect.

  Incorrect. No snapshot feature exists for ephemeral local NVMe disks. Snapshots are only supported for block volumes. Ephemeral local NVMe storage content is lost if the instance fails or is terminated.

• D. Correct.

  Correct. Object Storage or a persistent block volume is ideal for long-term or final data storage. Copying your results after processing is the safest way to avoid data loss when using ephemeral storage.

Correct answer: A

• A. Correct.

  Correct. DenseIO shapes include locally attached NVMe storage, which provides high IOPS and low latency for ephemeral workloads. Since the dataset is not needed after processing, using local NVMe drives is ideal for high-performance temporary storage needs.

• B. Incorrect.

  Incorrect. Standard shapes rely on block volumes for additional storage, which are persistent network-attached volumes. While OCI block volumes are reliable, they do not match the ultra-low latency of locally attached NVMe drives, which is crucial for HPC workloads.

• C. Incorrect.

  Incorrect. GPU shapes provide specialized hardware accelerators but do not necessarily include high-performance local NVMe storage by default. Storing ephemeral data in Object Storage will introduce higher latency compared to locally attached NVMe.

• D. Incorrect.

  Incorrect. Flexible shapes allow you to customize the OCPU and memory count, but this option alone does not provide ultra-low-latency, locally attached storage. Increasing memory can help with caching, but it does not replace the high IOPS required for HPC ephemeral datasets.

Correct answers: A, D

• A. Correct.

  Option 1 is correct. By creating an instance pool in a regional subnet and defining auto-scaling rules based on CPU utilization, you can dynamically scale out or in. Distributing instances across multiple availability domains ensures high availability.

• B. Incorrect.

  Option 2 is incorrect. Relying on a single large instance in one domain introduces a single point of failure, which is not recommended for high availability or cost optimization.

• C. Incorrect.

  Option 3 is incorrect. Ephemeral local NVMe storage is not suitable for persisting critical database data because it does not survive instance termination, thus compromising data reliability.

• D. Correct.

  Option 4 is correct. Using an OCI Load Balancer with health checks and directing traffic to the instance pool ensures that requests only go to healthy instances and that traffic is automatically distributed across multiple availability domains.

Correct answer: D

• A. Incorrect.

  Incorrect. While VM.Standard shapes can offer considerable flexibility and OCPU configurations, they do not provide the dedicated cluster networking (RDMA) capabilities required for large-scale HPC workloads.

• B. Incorrect.

  Incorrect. GPU shapes are ideal for workloads specifically optimized for GPU acceleration (e.g., AI/ML). While they can deliver strong parallel performance, they do not inherently provide the specialized low-latency interconnect (RDMA) that HPC shapes offer for tightly coupled computations across multiple nodes.

• C. Incorrect.

  Incorrect. A BM.Standard shape might deliver good performance, but it does not include built-in RDMA for ultra-low-latency communication across a large cluster of nodes. It's typically used for high-performance applications that do not require specialized HPC networking.

• D. Correct.

  Correct. HPC.BM shapes are designed especially for high-performance computing clusters on OCI. They provide RDMA-based cluster networking, enabling the low latency and high throughput critical for large-scale distributed simulations such as weather modeling.

Correct answer: B

• A. Incorrect.

  Option 1 (VM.Standard.E4.Flex): While this flexible shape allows you to configure CPU and memory, it does not offer the specialized low-latency cluster networking typically required for HPC workloads. It also lacks built-in local ephemeral storage critical for high-throughput scratch data.

• B. Correct.

  Option 2 (BM.HPC2.36): This Bare Metal HPC shape includes specialized cluster networking with low latency and provides local NVMe-based ephemeral storage. It is designed for CPU-intensive HPC workloads, making it ideal for large-scale simulations that require parallel processing.

• C. Incorrect.

  Option 3 (VM.GPU3.Flex): Although powerful for machine learning or graphics workloads, a GPU-focused shape is not necessary for a purely CPU-based HPC simulation. It also may not provide the specialized HPC networking stack and local ephemeral storage needed.

• D. Incorrect.

  Option 4 (BM.Standard.E3.128): While this is a robust Bare Metal shape with significant CPU and memory resources, it does not include low-latency cluster networking tailored to HPC environments or ephemeral local NVMe storage specifically optimized for HPC.

Correct answer: B

• A. Incorrect.

  Option 1 (VM.Standard3.Flex): While flexible shapes allow you to choose the number of OCPUs and memory, they do not provide large amounts of local NVMe storage for high IOPS. You would need to rely on block volumes, which may not deliver the same performance for temporary scratch space.

• B. Correct.

  Option 2 (BM.DenseIO2.52): This bare metal shape includes high-performance NVMe SSDs with substantial local storage, delivering very high IOPS. It is ideal for compute jobs requiring large amounts of ephemeral data storage and quick read/write operations, making it the best fit for the scenario.

• C. Incorrect.

  Option 3 (HPC2.36): HPC shapes are optimized for high-performance computing tasks, especially those requiring low-latency cluster networking. Although they provide strong compute capabilities, they do not focus on large local NVMe storage for big data workloads. Therefore, they are less suitable for this scenario where local storage performance is crucial.

• D. Incorrect.

  Option 4 (BM.GPU3.8): This shape is targeted at GPU-intensive workloads such as machine learning or rendering. While it includes powerful GPUs, it is not the most cost-effective or optimized choice for high-volume data processing using local storage.

Correct answer: D

• A. Incorrect.

  Option 1 (VM.Standard.E4.flex): While manually adjustable CPU and memory resources can help optimize cost and performance for various workloads, VM.Standard.E4.flex does not provide RDMA-based cluster networking or built-in ephemeral local NVMe storage. It's not ideal for intensive HPC simulations that need low-latency interconnects and high-throughput local disk operations.

• B. Incorrect.

  Option 2 (BM.Standard.E4.128): This bare metal shape offers a large number of CPU cores but does not feature HPC-specific RDMA cluster networking, nor does it come with ephemeral local NVMe storage. It would require attaching block volumes separately, which can add latency compared to local NVMe.

• C. Incorrect.

  Option 3 (BM.GPU3.8): While GPU shapes are excellent for machine learning and compute-accelerated workloads, they do not specifically target the low-latency, HPC cluster networking offered by HPC shapes. They also may not include the same scale of ephemeral local NVMe necessary for large-scale HPC I/O needs.

• D. Correct.

  Option 4 (BM.HPC3.36): This is an HPC-optimized bare metal shape that includes high-bandwidth RDMA cluster networking and ephemeral local NVMe storage, making it suitable for running large-scale simulations that require low-latency, high-throughput file operations. It is specifically designed for HPC workloads.

Correct answer: D

• A. Incorrect.

  Option 1 (VM.Standard3.Flex): While flexible shapes allow you to configure OCPUs and memory, they are not optimized for tightly coupled HPC workloads with low-latency interconnect. They also do not provide dedicated HPC networking features.

• B. Incorrect.

  Option 2 (BM.GPU3.8): This shape provides GPU acceleration, which is suitable for workloads like machine learning or high-end graphics. However, traditional CFD simulations often benefit more from HPC-specific CPU-based resources with RDMA networking, making a GPU-focused shape not the best fit.

• C. Incorrect.

  Option 3 (BM.Standard3.16): This bare metal shape offers dedicated resources but lacks the specialized RDMA-based cluster networking features that HPC shapes provide for scaling computational workloads across multiple nodes.

• D. Correct.

  Option 4 (BM.HPC2.36): This is the correct choice. HPC shapes in OCI are designed for highly parallel, compute-intensive tasks. They include low-latency RDMA networking and local NVMe storage, making them optimal for CFD simulations and other HPC cluster workloads.