Free PDF 2026 DASCA Useful SDS Training Material

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The DASCA SDS practice questions come with three easy-to-use and install formats. The certification for the DASCA SDS exam is a valuable, well-recognized professional credential. You can develop your skills and become a recognized specialist with the Senior Data Scientist SDS Certification in addition to learning about new technology requirements.

DASCA Senior Data Scientist Sample Questions (Q75-Q80):

NEW QUESTION # 75
Machine learning can be used in:

Answer: C

Explanation:
Machine Learning has broad applications across industries and technologies:
Fraud Detection (Option A): Detecting anomalies in financial transactions, credit card usage, and cybersecurity threats.
Web Search Results (Option B): Ranking algorithms (e.g., Google's PageRank enhanced by ML techniques) improve relevance of search queries.
Real-time Ads (Option C): Online ad systems use reinforcement learning and recommendation models to target ads dynamically.
Pattern & Image Recognition (Option D): ML (especially deep learning) powers facial recognition, handwriting recognition, medical imaging, etc.
Since ML is used in all these applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning Across Domains.


NEW QUESTION # 76
Which of the following is an SLAs specification in case of Internet Service Provider (ISP)?

Answer: A

Explanation:
Service Level Agreements (SLAs) define performance commitments between service providers (like ISPs) and customers. Key SLA metrics include:
MTBF (Option A): Measures reliability by defining the expected average time between service failures.
MTTR (Option B): Measures availability by defining how quickly service can be restored after a failure.
TAT (Option C): Measures responsiveness in resolving customer requests or incidents.
All three are standard SLA performance specifications. Hence, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering and IT Practices: SLAs and Performance Metrics.


NEW QUESTION # 77
In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:

Answer: C

Explanation:
Unsupervised learning is a machine learning approach where no labeled outputs are provided. The algorithm discovers patterns or structures directly from raw data.
Option A (Detecting regularities): Correct. Unsupervised learning identifies hidden structures such as clusters, associations, and dimensionality reductions (e.g., k-means clustering, PCA).
Option B (Detecting irregularities): Correct. Outlier detection is also a part of unsupervised learning, often used in anomaly detection (e.g., fraud detection, intrusion detection).
Option C: Correct, since unsupervised learning helps detect both regularities (clusters, groups) and irregularities (outliers, anomalies).
Thus, the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Unsupervised Learning: Clustering, Anomaly Detection, and Pattern Discovery.


NEW QUESTION # 78
Which of the following is NOT a cluster management tool?

Answer: C

Explanation:
Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.
Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.
Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.
Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.
Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.
Thus, the correct answer is Option D (Apache Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Hadoop Tools & Cluster Management.


NEW QUESTION # 79
Which of the following is a trend analysis component of time series decomposition?

Answer: D

Explanation:
Time series decomposition breaks down data into components to better understand underlying patterns and support forecasting. The main components are:
Trend: Long-term progression (upward or downward).
Seasonal: Repeating short-term patterns (e.g., monthly or quarterly).
Cyclical (Option A): Medium- to long-term cycles (e.g., business cycles).
Irregular/Residual (Option C): Random, unpredictable variations.
Since trend analysis involves examining cyclical, seasonal, and irregular components, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Time Series Decomposition and Trend Analysis.


NEW QUESTION # 80
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