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CompTIA DataX Certification Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
During EDA, a data scientist wants to look for patterns, such as linearity, in the data. Which of the following plots should the data scientist use?

A. Violin
B. Box-and-whisker
C. Scatter
D. Q-Q

Answer: C

Explanation:
# Scatter plots are used to examine relationships and trends between two numeric variables. They are especially effective at revealing linear (or nonlinear) patterns, clusters, and outliers.
Why the other options are incorrect:
* A: Violin plots show distribution and density, not relationships.
* B: Box plots compare distributions across groups but don't reveal linearity.
* D: Q-Q plots test normality, not variable relationships.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 1.2:"Scatter plots are commonly used during EDA to identify correlations, linearity, and outliers between two continuous variables."
* Data Science Fundamentals, Chapter 2 - EDA Techniques:"To assess linear trends and relationships, scatter plots provide a direct visual assessment between variables."

NEW QUESTION # 70
A data scientist is presenting the recommendations from a monthslong modeling and experiment process to the company's Chief Executive Officer. Which of the following is the best set of artifacts to include in the presentation?

A. Methodology, code snippets, findings, data tables, and p-values
B. Results, recommendations, justifications, and clear charts
C. Methods, data overview, results, recommendations, and charts
D. Recommendation, charts, justifications, code reviews, and results

Answer: B

Explanation:
# For executive-level presentations, the focus should be on strategic outcomes. Therefore, concise results, clear actionable recommendations, visual summaries (charts), and minimal justifications are best. Technical details such as p-values, code, or full methods are too granular.
Why the other options are incorrect:
* A: Too method-heavy for executive audiences.
* C: Includes code reviews - not suitable for a CEO.
* D: Overly technical for high-level stakeholders.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"Executive communication should focus on outcome-driven recommendations, high-level insights, and actionable visuals."
* Harvard Business Review - Communicating Data to Executives:"Avoid technical detail. Use visuals and clearly stated recommendations supported by business-focused justifications."

NEW QUESTION # 71
Which of the following environmental changes is most likely to resolve a memory constraint error when running a complex model using distributed computing?

A. Moving model processing to an edge deployment
B. Migrating to a cloud deployment
C. Converting an on-premises deployment to a containerized deployment
D. Adding nodes to a cluster deployment

Answer: D

Explanation:
When running a model on a distributed system, encountering memory constraint errors indicates that the current nodes in the cluster do not have enough memory to handle the model. The most scalable and immediate solution is:
# Adding Nodes to a Cluster Deployment - This increases the total available memory and compute power. In distributed computing environments like Apache Spark or Hadoop, horizontal scaling via node addition is a standard remedy for resource bottlenecks, including memory limitations.
Why the other options are incorrect:
* A. Containerizing doesn't inherently solve memory issues unless paired with resource upgrades.
* B. Cloud migration may offer more resources, but without scaling configuration, memory limits may persist.
* C. Edge deployment is for low-latency, local processing - often with less memory, not more.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2 (Infrastructure & Scaling):"To resolve memory limitations in distributed systems, scaling out by adding nodes is the most direct and cost- effective method."
* Data Engineering Fundamentals (Cloud/Distributed Systems):"Cluster resource constraints (e.g., memory) can be mitigated by increasing node count, enabling parallel execution and expanded memory pools."
-

NEW QUESTION # 72
Which of the following methods should a data scientist use just before switching to a potential replacement model?

A. Containerization
B. Performance monitoring
C. A/B testing
D. CI/CD

Answer: C

Explanation:
# A/B testing allows a controlled experiment comparing the performance of two models - the current (A) vs.
the candidate (B) - on live data. It's an industry best practice to validate real-world behavior before full replacement.
Why the other options are incorrect:
* B: Performance monitoring helps detect drift but doesn't directly compare models.
* C: CI/CD automates deployment but doesn't evaluate performance differences.
* D: Containerization packages the model but doesn't test it comparatively.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 5.5:"A/B testing is a recommended approach to validate model performance before switching versions in production."
* ML System Operations Guide, Chapter 6:"Use A/B testing to ensure new models outperform baselines before full rollout."
-

NEW QUESTION # 73
A data analyst wants to use compression on an analyzed data set and send it to a new destination for further processing. Which of the following issues will most likely occur?

A. Operating system support will be missing.
B. Library dependency will be missing.
C. Server CPU usage will be too high.
D. Server memory usage will be too high.

Answer: C

Explanation:
# Compression is a CPU-intensive process because it requires encoding data into a smaller format, often involving complex algorithms. While memory use is usually moderate, CPU usage can spike significantly, especially during real-time compression or large dataset processing.
Why the other options are incorrect:
* A: Library issues are possible but not the most likely issue in compression.
* C: Most operating systems support common compression formats (e.g., .zip, .gz).
* D: Memory usage is generally lower than CPU usage during compression.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.4:"Compression is compute-intensive and may result in increased CPU utilization, particularly on shared servers or during large batch processes."
* Cloud Data Engineering Guide, Chapter 9:"High CPU usage is a common bottleneck in data compression and decompression processes, especially at scale."
-

NEW QUESTION # 74
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