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NEW QUESTION # 14
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?
- A. OCI Speech
- B. OCI Translation
- C. OCI Language
- D. OCI Anomaly Detection
Answer: A
NEW QUESTION # 15
You are building a model and need input that represents data as morning, afternoon, or evening. However, the data contains a time stamp. What part of the Data Science life cycle would you be in when creating the new variable?
- A. Data access
- B. Model validation
- C. Model type selection
- D. Feature engineering
Answer: B
NEW QUESTION # 16
Which Oracle Cloud Infrastructure (OCI) service should you use to create and run Spark applications using ADS?
- A. Vault
- B. Analytics Cloud
- C. Data Integration
- D. Data Flow
Answer: D
NEW QUESTION # 17
You have just received a new data set from a colleague. You want to quickly find out summary information about the data set, such as the types of features, the total number of observations, and distributions of the data. Which Accelerated Data Science (ADS) SDK method from the ADSDataset class would you use?
- A. compute ()
- B. to_xgb ()
- C. show_corr()
- D. show_in_notebook ()
Answer: D
NEW QUESTION # 18
You have a complex Python code project that could benefit from using Data Science Jobs as it is a repeatable machine learning model training task. The project contains many subfolders and classes.
What is the best way to run this project as a Job?
- A. ZIP the entire code project folder and upload it as a Job artifact. Jobs automatically identifies the_main_ top level where the code is run.
- B. ZIP the entire code project folder, upload it as a Job artifact on job creation, and set JOB_RUN_ENTRYPOINT to point to the main executable file.
- C. ZIP the entire code project folder and upload it as a Job artifact on job creation. Jobs identifies the main executable file automatically.
- D. Rewrite your code so that it is a single executable Python or Bash/Shell script file.
Answer: B
NEW QUESTION # 19
During a job run, you receive an error message that no space is left on your disk device. To solve the problem, you must increase the size of the job storage. What would be the most effi-cient way to do this with Data Science Jobs?
- A. Edit the job, change the size of the storage of your job, and start a new job run.
- B. On the job run, set the environment variable that helps increase the size of the storage.
- C. Your code using too much disk space. Refactor the code to identify the problem.
- D. Create a new job with increased storage size and then run the job.
Answer: A
NEW QUESTION # 20
3. When preparing your model artifact to save it to the Oracle Cloud Infrastructure (OCI) Data Science model catalog, you create a score.py file. What is the purpose of the score.py file?
- A. Define the inference server dependencies
- B. Configure the deployment infrastructure.
- C. Define the compute scaling strategy.
- D. Execute the inference logic code.
Answer: D
NEW QUESTION # 21
As a data scientist, you are working on a global health data set that has data from more than 50 countries. You want to encode three features, such as 'countries', 'race', and 'body organ' as categories. Which option would you use to encode the categorical feature?
- A. OneHotEncoder()
- B. DataFramLabelEncode()
- C. auto_transform()
- D. show_in_notebook()
Answer: B
NEW QUESTION # 22
You are using Oracle Cloud Infrastructure (OCI) Anomaly Detection to train a model to detect anomalies in pump sensor data. How does the required False Alarm Probability setting affect an anomaly detection model?
- A. It adds a score to each signal indicating the probability that its a false alarm.
- B. It determines how many false alarms occur before an error message is generated.
- C. It changes the sensitivity of the model to detecting anomalies.
- D. It is used to disable the reporting of false alarms.
Answer: C
NEW QUESTION # 23
You are a computer vision engineer building an image recognition model. You decide to use Oracle Data Labeling to annotate your image data. Which of the following THREE are possible ways to annotate an image in Data Labeling?
- A. Adding a single label to an image.
- B. Adding labels to an image using object detection, by drawing bounding boxes to an im-age.
- C. Adding multiple labels to an image.
- D. Adding labels to an image by drawing bounding box to an image, is not supported by Data Labeling
- E. Adding labels to image using semantic segmentation, by drawing multiple bounding boxes to an image.
Answer: A,B,C
NEW QUESTION # 24
You have just received a new data set from a colleague. You want to quickly find out summary information about the data set, such as the types of features, total number of observations, and data distributions, Which Accelerated Data Science (ADS) SDK method from the AD&Dataset class would you use?
- A. Show_in_notebook{}
- B. To_xgb{}
- C. Show_corr{}
- D. Compute{}
Answer: A
NEW QUESTION # 25
What preparation steps are required to access an Oracle AI service SDK from a Data Science notebook session?
- A. Import the REST API.
- B. Create and upload score.py and runtime.yaml.
- C. Create and upload the APIsigning key and config file.
- D. Call the ADS command to enable AI integration
Answer: C
NEW QUESTION # 26
As a data scientist, you create models for cancer prediction based on mammographic images.
The correct identification is very crucial in this case. After evaluating two models, you arrive at the following confusion matrix.
Model 1 has Test accuracy is 80% and recall is 70%.
* Model 2 has Test accuracy is 75% and recall is 85%.
Which model would you prefer and why?
- A. Model 1, because recall has lesser impact on predictions in this use case
- B. Model 2, because recall has more impact on predictions in this use se.
- C. Model 2, because recall is high.
- D. Model 1, because the test accuracy is high.
Answer: B
NEW QUESTION # 27
As a data scientist, you are tasked with creating a model training job that is expected to take different hyperparameter values on every run. What is the most efficient way to set those parameters with Oracle Data Science Jobs?
- A. Create your code to expect different parameters either as environment variables or as command line arguments, which are set on every job run with different values.
- B. Create a new job every time you need to run your code and pass the parameters as environment variables.
- C. Create your code to expect different parameters as command line arguments and create a new job every time you run the code.
- D. Create a new job by setting the required parameters in your code and create a new job for every code change.
Answer: A
NEW QUESTION # 28
You have an embarrassingly parallel or distributed batch job on a large amount of data that you consider running using Data Science Jobs. What would be the best approach to run the workload?
- A. Create the job in Data Science Jobs and then start the number of simultaneous jobs runs required for your workload.
- B. Create the job in Data Science Jobs and start a job run. When it is done, start a new job run until you achieve the number of runs required.
- C. Create a new job for every job run that you have to run in parallel, because the Data Science Jobs service can have only one job run per job.
- D. Reconfigure the job run because Data Science Jobs does not support embarrassingly parallel workloads.
Answer: A
NEW QUESTION # 29
As a data scientist, you have stored sensitive data in a database. You need to protect this data by using a master encryption algorithm, which uses symmetric keys. Which master encryption algorithm would you choose in the Oracle Cloud Infrastructure (OCI) Vault service?
- A. Rivert-Shamir-Adleman Keys
- B. Elliptical Curve Cryptography Digital Signature Algorithm
- C. Triple Data Encryption Standard Algorithm
- D. Advanced Encryption Standard Keys
Answer: D
NEW QUESTION # 30
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