CompTIA DY0-001 Practice Test Questions Answers

→ 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.”

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→ In the proof-of-concept phase, the first practical step is model selection — identifying which modeling technique is most appropriate based on the nature of the problem, data, and business goal. Literature reviews are helpful but usually precede model experimentation.

Why the other options are incorrect:

A: Literature review informs planning but isn't the first hands-on step.

B: Performance evaluation comes after models are built.

C: Hyperparameter tuning applies after a model is chosen.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 5.1:“Model selection is a critical step during early prototyping when evaluating different algorithms for feasibility.”

CRISP-DM Framework – Modeling Phase:“Selecting candidate models is the first step in model development after understanding the data.”

→ In real-time systems, low latency (short run time) is critical. While the Artificial Neural Network provides the highest accuracy, its 12-minute runtime makes it unsuitable for real-time inference. Random forest is the fastest but offers the lowest accuracy.

XGBoost provides an excellent balance between runtime (5 minutes) and accuracy (90%). It’s well-optimized for performance and scalability, and thus is a strong candidate for real-time classification when balancing both efficiency and predictive quality.

Why the other options are less ideal:

B: Random forest is faster but significantly less accurate.

C: Decision trees have longer run time than XGBoost with only a 2% accuracy improvement.

D: Artificial neural network has the highest accuracy but is too slow for real-time applications.

Official References:

CompTIA DataX (DY0-001) Official Study Guide – Section 4.3:“In real-time applications, model selection involves a trade-off between accuracy and inference speed. XGBoost offers competitive accuracy with efficient runtime.”

Machine Learning Systems Design Guide, Chapter 7:“XGBoost is well-suited for real-time systems due to its balance of model complexity and fast prediction times.”

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→ LASSO (Least Absolute Shrinkage and Selection Operator) regression minimizes the squared residuals (e²), just like OLS, but adds an L1 penalty to encourage sparsity in the coefficients. Thus, the residual component minimized is still the sum of squared errors.

Why the other options are incorrect:

A: |e| is absolute error, not used in standard LASSO objective.

B: e is the error term, but minimization applies to its squared version.

C: Minimizing to exactly 0 is idealistic but not realistic.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 3.3:“LASSO minimizes squared errors with an additional L1 regularization term.”

Elements of Statistical Learning, Chapter 6:“LASSO regression uses the same residual sum of squares (e²) as OLS for error measurement, with an added constraint.”

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→ Edit distance (also known as Levenshtein distance) measures how many single-character edits (insertions, deletions, or substitutions) are needed to transform one string into another. It’s a common metric for assessing string similarity, especially in natural language processing (NLP) tasks.

Why the other options are incorrect:

A: Word clouds visualize word frequency, not similarity.

C: String indexing is a method for referencing string positions, not comparison.

D: k-NN is a classification algorithm, not a string similarity measure.

Official References:

CompTIA DataX (DY0-001) Study Guide – Section 6.3:“Edit distance is a key similarity metric in text comparison tasks, particularly in cleaning or matching string records.”

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