SDS Exam Dumps - Senior Data Scientist

Apache Spark is an open-source distributed computing framework widely used for big data processing and machine learning pipelines.

The core implementation of Spark is written in Scala (Option A), which runs on the JVM (Java Virtual Machine).

Spark also provides APIs for Java, Python (PySpark), R, and SQL, but its native language is Scala.

Options C (C) and D (C++) are incorrect; Spark is not written in these languages.

Python (Option E) is a supported API, but Spark itself is not written in Python.

Thus, the correct answer is Scala (Option A).

But per MCQ single-choice format → Answer: A (though ideally A, B, C are correct).

A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:

Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.

Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.

Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.

Option D: Incorrect. Data lakes improve agility, not reduce it.

Option E: Incorrect, since multiple true statements exist.

Thus, the correct choice per DASCA context is Option A (with B and C also being true).

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).

Semi-structured data falls between structured data (e.g., relational databases with fixed schema) and unstructured data (e.g., free text, audio, video). It typically includes irregular or flexible schema information, such as XML, JSON, email data, or log files.

Option A (Database systems): Correct, databases may hold semi-structured content (e.g., JSON or XML columns).

Option B (File systems): Correct, file-based storage (logs, JSON, Avro, CSV) often contains semi-structured data.

Option C (Scientific data): Correct, many scientific applications generate semi-structured data formats (sensor readings, genomic sequences, etc.).

Option D (Schema-full data): Correct Answer. Schema-full (strict schema-defined relational tables) represent structured data, not semi-structured.

Thus, semi-structured data does NOT include schema-full data.

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).

The DevOps movement evolved as a natural extension of the Agile methodology.

Agile (Option A): Agile emphasizes iterative development, collaboration, and flexibility. While Agile improved software development speed, it created challenges in integrating development with IT operations. DevOps emerged to address this by bringing operations into the Agile cycle — enabling continuous integration, delivery, and deployment.

Waterfall (Option B): Incorrect. Waterfall is a rigid, sequential methodology, fundamentally opposite to the DevOps philosophy.

Promise-based algorithms (Option C): Not a methodology — irrelevant here.

Test-driven development and model-driven development (Option D): While these practices support DevOps, they are not the origin of the movement.

Thus, the DevOps movement is an outgrowth of Agile methodology.

Scribe is a server developed by Facebook for aggregating log data streamed in real time from many servers.

Its primary aim was reliability and simplicity in collecting logs at scale.

Unlike other systems, Scribe was designed to avoid complex or extensive protocols and to ensure lightweight log transfer.

It also avoids unnecessary high disk usage, relying instead on efficient pipelines.

Thus, the correct answer is Option D (None of the above).

Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.

Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12–18 months to establish baselines.

Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.

Thus, both statements are correct → Option C (Both i and ii).