You have just completed analyzing a set of images by using Oracle Cloud Infrastructure (OCI) Data Labeling, and you want to export the annotated data. Which TWO formats are supported?
Reasoning: B aligns with OCI Language’s text custom models. Conclusion: B is correct. OCI Language documentation states: “Custom models can be trained for text classification and Named Entity Recognition (NER) using your data.” Image tasks (A, D) are for Vision, and sentiment (C) is pretrained —only B fits OCI Language’s scope. : Oracle Cloud Infrastructure Language Documentation, "Custom Model Training". You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?
Detailed Answer in Step -by-Step Solution: Objective: Identify ADS class for dataset access (e.g., scikit -learn). Evaluate Options: A: DataLabeling —Not an ADS class. B: DatasetBrowser —Not real. C: SecretKeeper —Credentials, not data. D: DatasetFactory —Loads datasets (e.g., open()) —correct. Reasoning: DatasetFactory simplifies library dataset access. Conclusion: D is correct. OCI documentation states: “DatasetFactory (D) in ADS SDK accesses datasets from libraries like scikit - learn (e.g., DatasetFactory.open('sklearn.datasets:load_iris')).” A, B, and C don’t exist or apply —only D fits. : Oracle Cloud Infrastructure ADS SDK Documentation, "DatasetFactory". You are a data scientist trying to load data into your notebook session. You understand that Accelerated Data Science (ADS) SDK supports loading various data formats. Which of the following THREE are ADS -supported data formats?
need some additional Python libraries for processing genome sequencing data. Which of the following THREE statements are correct with respect to installing additional Python libraries to process the data?
Objective: Specify third -party libraries for model deployment. Understand Artifacts: runtime.yaml defines runtime; score.py handles logic. Evaluate Options: A: Not a standard file —incorrect. B: Inference code —not for dependencies. C: Defines conda env with dependencies —correct. D: Pip list—not used in OCI conda deployments. Reasoning: runtime.yaml points to a conda env with all libraries. Conclusion: C is correct. OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third -party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist —C fixes the issue. : Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml". You are a data scientist working for a utilities company. You have developed an algorithm that detects anomalies from a utility reader in the grid. The size of the model artifact is about 2 GB, and you are trying to store it in the model catalog. Which THREE interfaces could you use to save the model artifact into the model catalog?
➢ TOTAL QUESTIONS: 308 A bike sharing platform has collected user commute data for the past 3 years. For increasing profitability and making useful inferences, a machine learning model needs to be built from the accumulated data. Which of the following options has the correct order of the required machine learning tasks for building a model?
