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Oracle 1Z0-1110-26 Exam Syllabus Topics:

Oracle Cloud Infrastructure Data Science Professional Sample Questions:

1. You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor dat a. What are you trying to determine? How does the required False Alarm Probability setting affect an anomaly detection model?

A) It determines how many false alarms occur before an error message is generated
B) It adds a score to each signal indicating the probability that it’s a false alarm
C) It is used to disable the reporting of false alarms
D) It changes the sensitivity of the model to detecting anomalies

2. 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?

A) Accelerated Data Science (ADS) Software Development Kit (SDK)
B) Console
C) ODSC CLI
D) OCI Python SDK
E) Oracle Cloud Infrastructure (OCI) Command Line Interface (CLI)
F) Git CLI

3. What is the name of the machine learning library used in Apache Spark?

A) HadoopML
B) GraphX
C) MLib
D) Structured Streaming

4. You are a data scientist building a pipeline in the Oracle Cloud Infrastructure (OCI) Data Science service for your machine learning project. You want to optimize the pipeline completion time by running some steps in parallel. Which statement is true about running pipeline steps in parallel?

A) Steps in a pipeline can be run only sequentially
B) All pipeline steps are always run in parallel
C) Parallel steps cannot be run if they are completely independent of each other
D) Pipeline steps can be run in sequence or in parallel, as long as they create a directed acyclic graph (DAG)

5. Six months ago, you created and deployed a model that predicts customer churn for a call centre. Initially, it was yielding quality predictions. However, over the last two months, users are questioning the credibility of the predictions. Which TWO methods would you employ to verify the accuracy of the model?

A) Drift monitoring
B) Validate the model using recent data
C) Operational monitoring
D) Retrain the model
E) Redeploy the model

Solutions: