Reasoning: C (preinstalled) and D (installable) are practical access points. Conclusion: C and D are correct. OCI documentation states: “The ADS SDK is available in OCI Data Science notebook sessions via preinstalled conda environments (C) and can be installed from PyPI (D) using pip install oracle -ads.” Big Data (A), Machine Learning (B), and ADW (E) don’t host ADS —only C and D apply. : Oracle Cloud Infrastructure Data Science Documentation, "ADS SDK Installation". You are attempting to save a model from a notebook session to the model catalog by using ADS SDK, with resource principal as the authentication signer, and you get a 404 authentication error. Which TWO should you look for to ensure permissions are set up correctly?
and E applies to user auth —not resource principal. A 404 error flags missing auth, fixed by A and C. : Oracle Cloud Infrastructure Data Science Documentation, "Using Resource Principals with ADS SDK". You are a data scientist working inside a notebook session and you attempt to pip install a package from a public repository that is not included in your conda environment. After running this command, you get a network timeout error. What might be missing from your network configuration?
possible, even at an increased cost. C. Start with a small shape and monitor the utilization metrics and time required to complete the model training. If the compute shape is fully utilized, change to compute that has more resources and rerun the job. Repeat the process until the processing time does not improve. D. Start with a random compute shape and monitor the utilization metrics and time required to finish the model training. Perform model training optimization and performance tests in advance to identify the right compute shape before running the model training as a job. Explanation: Detailed Answer in Step -by-Step Solution: Objective: Find optimal compute shape balancing cost and time. Approach: Iterative testing with metrics (e.g., CPU/memory usage, runtime). Evaluate Options: A: Tuning parameters when underutilized —focuses on model, not shape optimization. B: Strongest shape —Costly, ignores balance; overkill likely. C: Scale up from small shape when fully utilized —Balances cost/time effectively. D: Random start with pre-tests—Unsystematic and inefficient. Reasoning: C incrementally increases resources based on utilization, optimizing both factors. Conclusion: C is correct. OCI documentation advises: “To optimize compute shape for Jobs, start with a small shape, monitor utilization (e.g., CPU, memory) and runtime via OCI Monitoring. If fully utilized, scale up until performance plateaus —balancing cost and speed.” A misfocuses on model tuning, B wastes cost, and D lacks structure —only C aligns with this method. : Oracle Cloud Infrastructure Data Science Documentation, "Optimizing ComputeShapes for Jobs". You are given a task of writing a program that sorts document images by language. Which Oracle AI Service would you use?
Reasoning: Images need OCR (Vision) then language detection (Language) —D fits the sorting task. Conclusion: D is correct. OCI Language “detects and classifies languages in text,” often paired with OCI Vision’s OCR to process document images. Vision (B) extracts text, but Language (D) sorts by language —Digital Assistant (A) and Speech (C) don’t apply. Documentation supports this workflow. : Oracle Cloud Infrastructure Language Documentation, "Language Detection". You are asked to prepare data for a custom -built model that requires transcribing Spanish video recordings into a readable text format with profane words identified. Which Oracle Cloud Service would you use?
Detailed Answer in Step -by-Step Solution: Objective: Identify ADS class for accessing datasets (e.g., scikit -learn). Evaluate Options: A: DatasetBrowser —Not an ADS class. B: DatasetFactory —Loads datasets from sources like scikit -learn —correct. C: ADSTuner —Hyperparameter tuning, not data access. D: SecretKeeper —Manages credentials, not datasets. Reasoning: DatasetFactory simplifies dataset loading (e.g., DatasetFactory.open()). Conclusion: B is correct. OCI documentation states: “DatasetFactory in ADS SDK provides methods to easily load datasets from libraries like scikit -learn or other sources (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A isn’t real, C tunes models, and D handles secrets —only B fits. : Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory". You are working in your notebook session and find that your notebook session does not have enough compute CPU and memory for your workload. How would you scale up your notebook session without losing your work?
: Oracle Cloud Infrastructure Data Science Documentation, "Scaling Notebook Sessions". The Oracle AutoML pipeline automates hyperparameter tuning by training the model with different parameters in parallel. You have created an instance of Oracle AutoML as oracle_automl and now you want an output with all the different trials performed by Oracle AutoML. Which of the following commands gives you the results of all trials?
