Oracle Cloud Infrastructure 2024 Data Science Professional Free Practice Test — 30 Questions

Question 1

In a project aimed at predicting customer churn for a subscription service, you discover that a significant portion of your dataset has missing values in the \'last_purchase_date\' feature. What is the most effective approach to handle these missing values while ensuring the integrity of your predictive model?

Accepted Answer

Use imputation techniques to fill in the missing values based on the average time since the last purchase for customers with similar profiles.

Question 2

In a project aimed at predicting stock prices based on historical data, a data scientist decides to implement a Recurrent Neural Network (RNN). However, during the training phase, they notice that the model struggles to learn from earlier time steps in the sequence, leading to poor performance on long-term predictions. Which of the following strategies would most effectively address this issue?

Accepted Answer

Implementing Long Short-Term Memory (LSTM) units to enhance the model's ability to capture long-term dependencies.

Question 3

In a large organization utilizing Oracle Cloud Infrastructure for data science projects, the data governance team is tasked with ensuring that sensitive customer data is both accessible for analysis and secure from unauthorized access. They are considering implementing a new policy that allows data scientists to access all datasets without restrictions, provided they complete a data security training program. What is the most appropriate course of action for the data governance team to take in this scenario?

Accepted Answer

Implement a role-based access control system that restricts data access based on the sensitivity of the data and the specific roles of the data scientists.

Question 4

A data scientist is evaluating two algorithms for processing a dataset of size $N$. The first algorithm has a time complexity of $T(N) = 3N^2 + 5N + 2$, while the second algorithm has a time complexity of $T\'(N) = 2N \log N + 4N + 1$. At what size of $N$ does the second algorithm become more efficient than the first, assuming both algorithms are equal at some point? What is the correct expression for the crossover point $N$ where $T(N) = T\'(N)$?

Accepted Answer

$N = \frac{5 + \sqrt{25 - 4(3)(2 - 1)}}{2(3)}$

Question 5

A data scientist is preparing a dataset for a machine learning model that includes a categorical feature representing customer segments, which has over 100 unique values. The scientist is considering different encoding techniques to transform this feature into a suitable format for the model. What is the most appropriate approach to handle this high cardinality categorical variable?

Accepted Answer

Use target encoding to replace each category with the average target value for that category, thereby reducing dimensionality while preserving information.

Question 6

A data scientist is tasked with analyzing customer feedback data collected from various sources, including surveys and social media. The data includes numerical ratings, textual comments, and binary indicators of satisfaction. To effectively analyze this data, the scientist needs to choose the appropriate data types and structures for storage and processing. Which combination of data types and structures would best facilitate this analysis while ensuring efficient data manipulation and retrieval?

Accepted Answer

Use a combination of integers for ratings, strings for comments, and a dictionary to map customer IDs to their respective feedback.

Question 7

A healthcare data scientist is evaluating a predictive model designed to identify patients at risk of a serious illness. After testing the model, they find that it has an accuracy of 90%, but upon deeper analysis, they discover that it has a precision of 60% and a recall of 30%. Given these metrics, which of the following statements best describes the implications of the model\'s performance?

Accepted Answer

The model is likely over-predicting the positive class, leading to a significant number of false positives, which could cause unnecessary concern among patients.

Question 8

A retail company is analyzing its monthly sales data over the past five years to forecast future sales. The data shows a consistent upward trend, with noticeable peaks during holiday seasons. The data scientist is considering whether to use an additive or multiplicative model for forecasting. Given the presence of both a trend and seasonal fluctuations, which modeling approach would be most appropriate for this scenario?

Accepted Answer

A multiplicative model, as it accounts for the interaction between the trend and seasonal components.

Question 9

A streaming service is looking to enhance its recommendation system to improve user engagement. They currently use a collaborative filtering approach but face challenges with new users who have not yet rated any content. To address this issue, the data science team is considering integrating a content-based filtering method. How would this integration most effectively resolve the cold start problem while maintaining the quality of recommendations?

Accepted Answer

By analyzing the attributes of the content that new users have shown interest in and recommending similar items based on those features.

Question 10

In a project aimed at improving the accuracy of a predictive model for customer churn, a data scientist is evaluating different approaches to enhance model performance. Considering the capabilities of Oracle Cloud Infrastructure, which strategy should the data scientist prioritize to ensure efficient model training and deployment?

Accepted Answer

Utilize Oracle Data Science to leverage built-in algorithms and collaborative features for streamlined model development and deployment.

Question 11

A data scientist is tasked with developing a predictive model using a dataset that contains a large number of features, many of which may not contribute significantly to the target variable. Given the high dimensionality of the data, which machine learning algorithm would be the most appropriate choice to ensure effective feature selection and model performance?

Accepted Answer

Support Vector Machine (SVM)

Question 12

A data scientist is working on a project that requires processing a large volume of streaming data using Apache Spark on Oracle Cloud Infrastructure. They need to ensure that their Spark application is optimized for performance and cost-effectiveness. Which approach should they take to achieve this goal?

Accepted Answer

Configure the Spark cluster with the appropriate instance types and optimize the number of executors based on the workload characteristics.

Question 13

A data scientist is working on a project that involves analyzing a large dataset containing customer transactions to predict future purchasing behavior. They need to preprocess the data, perform exploratory data analysis, and build a predictive model. Given the requirements of the project, which combination of libraries would be the most effective for handling data manipulation, numerical computations, and machine learning tasks?

Accepted Answer

Pandas for data manipulation, NumPy for numerical computations, and Scikit-learn for building the predictive model

Question 14

In a healthcare application, a data scientist is evaluating a binary classification model designed to predict whether patients have a certain disease. They generate an ROC curve and calculate the AUC. If the AUC is found to be 0.85, what does this indicate about the model\'s performance in distinguishing between patients with and without the disease?

Accepted Answer

The model has a good ability to discriminate between the two classes, indicating it is effective in identifying patients with the disease while minimizing false positives.

Question 15

In a retail company that is experiencing rapid growth, the management team is considering implementing a Big Data solution to better understand customer behavior and optimize inventory. They are particularly interested in how to effectively analyze the diverse types of data generated from various sources, such as online transactions, in-store purchases, and social media interactions. Which approach would best enable them to leverage Big Data for actionable insights?

Accepted Answer

Utilize a cloud-based data lake to store and process structured and unstructured data, allowing for scalable analytics and machine learning applications.

Question 16

A data scientist is tasked with improving the performance of a machine learning model that processes large datasets on Oracle Cloud Infrastructure. They are considering various optimization strategies. Which approach would most effectively enhance the model\'s performance by leveraging the cloud\'s capabilities?

Accepted Answer

Implementing parallel processing to handle data ingestion and model training simultaneously

Question 17

A data scientist is tasked with improving the accuracy of a predictive model for customer churn in a telecommunications company. The initial model, a single decision tree, shows high variance and is overfitting the training data. Considering the characteristics of the dataset and the need for a more robust solution, which ensemble method should the data scientist implement to effectively reduce variance and enhance predictive performance?

Accepted Answer

Bagging, as it reduces variance by averaging predictions from multiple models trained independently.

Question 18

A data scientist at a retail company is tasked with integrating a new machine learning model that predicts customer purchasing behavior with the existing customer relationship management (CRM) system. The integration must ensure real-time data synchronization and allow for automated updates to customer profiles based on model predictions. Which integration approach using Oracle Integration Cloud would be the most effective in achieving these requirements?

Accepted Answer

Utilize Oracle Integration Cloud's real-time integration capabilities to create a synchronous connection between the machine learning model and the CRM system, allowing immediate updates to customer profiles as predictions are made.

Question 19

In a scenario where a data scientist is tasked with improving the predictive accuracy of a model for a large and complex dataset with numerous features and potential overfitting issues, which advanced analytics technique would be the most suitable choice to implement?

Accepted Answer

Random Forest, as it effectively combines multiple decision trees to enhance accuracy and reduce overfitting.

Question 20

A company is planning to provision a block volume in Oracle Cloud Infrastructure. They estimate that they will need a volume of $8$ TB, with a cost of $75$ dollars per TB. If they expect a $15\%$ increase in storage needs over the next year, what will be the total cost of the new volume after the increase?

Accepted Answer

$690 \, 	ext{dollars}$

Question 21

A data scientist is analyzing the monthly sales figures of a new product over the past year. The sales data is heavily skewed due to a few exceptionally high sales months. In preparing a report for stakeholders, which summary statistic should the data scientist prioritize to accurately represent the typical sales performance?

Accepted Answer

Median

Question 22

In a retail company, a data scientist is assigned to analyze customer behavior and predict churn rates. The analysis requires extensive statistical modeling and the creation of detailed visualizations to present findings to the marketing team. Considering the requirements of this project, which programming language would be most suitable for the data scientist to utilize?

Accepted Answer

R

Question 23

In a data science project utilizing Oracle Cloud Infrastructure, a team of data scientists is collaborating on a machine learning model. They need to ensure that all team members can access and contribute to the project efficiently while maintaining data security and version control. Which collaboration feature in OCI Data Science would best support their needs?

Accepted Answer

The use of shared notebooks with role-based access control to manage permissions and track changes.

Question 24

A data scientist is analyzing customer satisfaction scores collected from a recent survey. The scores range from 1 to 10, but a few customers rated their experience as 1, significantly impacting the overall average. Given this situation, which summary statistic would best represent the typical customer experience without being influenced by the extreme ratings?

Accepted Answer

The median score of the responses

Question 25

A data scientist at a retail company is tasked with improving customer experience through personalized recommendations. They are considering various data sources for this project. Which data source would be the most effective for collecting insights that directly influence customer behavior and preferences?

Accepted Answer

Customer transaction history from the company's database

Question 26

A data engineer is tasked with configuring an OCI Big Data Service cluster for a new application that processes real-time streaming data from IoT devices. The application requires high throughput and low latency to ensure timely insights. Considering the architecture and capabilities of OCI Big Data Service, which configuration would best meet these requirements?

Accepted Answer

Configure a cluster with multiple nodes optimized for Apache Spark, utilizing in-memory processing to enhance speed and throughput.

Question 27

In a recent project, a data science team at a financial institution developed a machine learning model to assess creditworthiness. After deployment, they discovered that the model disproportionately denied loans to applicants from certain demographic groups. To address this issue, which responsible AI practice should the team prioritize to ensure fairness and equity in their model\'s outcomes?

Accepted Answer

Conduct a thorough bias audit of the training data and model outputs to identify and mitigate any sources of bias.

Question 28

In a data science project within Oracle Cloud Infrastructure, a team of data scientists is collaborating on a predictive analytics model. They need to ensure that all team members can access the latest version of the model and provide feedback efficiently. Which collaboration feature in OCI Data Science would best facilitate this process?

Accepted Answer

The use of shared notebooks with integrated version control to track changes and comments.

Question 29

A retail company is planning to implement Oracle Autonomous Database to manage its sales data, which experiences significant fluctuations during holiday seasons. They want to ensure that the database can handle peak loads efficiently while minimizing costs during quieter periods. Which feature of Oracle Autonomous Database would best support their needs in this scenario?

Accepted Answer

The ability to automatically scale resources based on workload demands.

Question 30

A data scientist has successfully built a predictive model to forecast customer churn for an e-commerce platform. As they prepare for deployment, they need to choose the most effective method to ensure the model can handle fluctuating traffic and provide real-time predictions. Considering the capabilities of Oracle Cloud Infrastructure, which deployment strategy should the data scientist choose to optimize performance and scalability?

Accepted Answer

Deploy the model using Oracle Cloud Infrastructure's managed services for machine learning, ensuring automatic scaling and integration with other OCI resources.

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