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[General] Free PDF Quiz CompTIA - Valid DY0-001 Sample Exam

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【General】 Free PDF Quiz CompTIA - Valid DY0-001 Sample Exam

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CompTIA DataX Certification Exam Sample Questions (Q83-Q88):NEW QUESTION # 83
The following graphic shows the results of an unsupervised, machine-learning clustering model:

k is the number of clusters, and n is the processing time required to run the model. Which of the following is the best value of k to optimize both accuracy and processing requirements?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: D
Explanation:
# The graph represents a classic "elbow curve," which is often used in clustering (e.g., k-means) to help determine the optimal number of clusters. The point where the curve starts to level off (the "elbow") reflects the best trade-off between model accuracy and processing efficiency.
In this graph, the elbow visually occurs around k = 10. Beyond that, the processing time continues to decrease, but the marginal gain in clustering quality (or drop in processing time) diminishes.
Why the other options are incorrect:
* A: k = 2 underfits the data - too few clusters.
* C & D: k = 15 or 20 provides minimal additional benefit in processing but may overcomplicate the model.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 4.2:"The elbow method identifies the optimal number of clusters where the rate of improvement drops significantly."
-

NEW QUESTION # 84
A data scientist receives an update on a business case about a machine that has thousands of error codes. The data scientist creates the following summary statistics profile while reviewing the logs for each machine:

| Number of machines observed | 3,000,000
| Number of unique error codes observed | 19,000
| Median number of unique codes per machine | 7
| Median number of error transactions | 45
Which of the following is the most likely concern with respect to data design for model ingestion?
  • A. Sparse matrix
  • B. Multivariate outliers
  • C. Granularity misalignment
  • D. Insufficient features
Answer: A
Explanation:
# With 19,000 unique error codes and only 7 codes per machine (on median), the data structure will likely consist of a very large number of binary features (e.g., one-hot encoded error codes), most of which will be 0 for any given machine. This leads to a sparse matrix-where the majority of elements are zero-which poses computational and modeling challenges.
Why the other options are incorrect:
* B: Granularity misalignment would mean mismatched levels (e.g., mixing daily and hourly data), which is not the issue here.
* C: There are many features (error codes), not too few.
* D: Multivariate outliers involve unusual combinations across features, not sparsity.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"High-cardinality categorical features can result in sparse matrices, especially when one-hot encoded for models."

NEW QUESTION # 85
Which of the following compute delivery models allows packaging of only critical dependencies while developing a reusable asset?
  • A. Edge devices
  • B. Virtual machines
  • C. Thin clients
  • D. Containers
Answer: D
Explanation:
# Containers (e.g., Docker) allow developers to package an application along with only the necessary runtime, libraries, and critical dependencies. This makes the asset lightweight, reusable, and portable across environments. Unlike virtual machines, containers share the host OS kernel and are far more efficient in packaging only what's essential.
Why the other options are incorrect:
* A: Thin clients refer to client-server models with minimal local processing - not relevant to dependency packaging.
* C: Virtual machines include an entire OS, leading to more overhead than necessary for reusable assets.
* D: Edge devices are hardware-based deployments typically used in IoT scenarios, not packaging tools.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2:"Containers enable consistent development environments by packaging applications and only critical dependencies, making them ideal for portability and reuse."
* Docker Documentation:"Containers package code and dependencies into a single unit of software, ensuring consistency across environments while minimizing overhead."
-

NEW QUESTION # 86
A data scientist is merging two tables. Table 1 contains employee IDs and roles. Table 2 contains employee IDs and team assignments. Which of the following is the best technique to combine these data sets?
  • A. left join on Table 1 with Table 2
  • B. outer join between Table 1 and Table 2
  • C. inner join between Table 1 and Table 2
  • D. right join on Table 1 with Table 2
Answer: C
Explanation:
# An inner join returns only those records that have matching keys (employee IDs in this case) in both tables.
Since each table provides a different attribute for the same entity (employee), an inner join is the most efficient and accurate method when focusing on employees present in both tables.
Why the other options are less ideal:
* B & C: Left or right joins would include unmatched data, which may lead to nulls.
* D: An outer join brings in all records from both tables and fills nulls where no matches exist, which may introduce irrelevant or incomplete entries.
Official References:
* CompTIA DataX (DY0-001) Official Study Guide - Section 5.2:"Inner joins are most appropriate when combining datasets with matching keys to retain only relevant, intersecting records."
* SQL for Data Analysts, Chapter 3:"Use inner joins when combining tables on a common key to include only matched data for analysis."
-

NEW QUESTION # 87
A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)
  • A. Normalization
  • B. Linearization
  • C. Pivoting
  • D. One-hot encoding
  • E. Scaling
  • F. Label encoding
Answer: D,F
Explanation:
# Categorical variables must be transformed into numerical form for most machine learning models. Two standard approaches:
* One-hot encoding: Converts each category into a separate binary column (useful for nominal variables).
* Label encoding: Converts categories into integers (useful for ordinal or tree-based models).
Why other options are incorrect:
* A & E: Normalization and scaling are used for continuous variables, not categorical.
* C: Linearization refers to transforming relationships, not categorical conversion.
* F: Pivoting rearranges data structure but doesn't encode categories.
Official References:
* CompTIA DataX (DY0-001) Study Guide - Section 3.3:"Label encoding and one-hot encoding are common transformations applied to categorical variables to enable model compatibility."
-

NEW QUESTION # 88
......
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