SDS試験攻略 & SDS試験参考書良いサイトは、高品質のSDS信頼できるダンプトレントを生成します。 関連製品を購入する場合は、この会社に力があるかどうか、製品が有効かどうかを明確にする必要があります。 SDS信頼できるダンプトレント。 一部の企業は、低価格の製品による素晴らしい販売量を持ち、彼らの質問と回答はインターネットで収集されますが、それは非常に不正確です。 本当に一発で試験に合格したい場合は、注意が必要です。 高品質のDASCA SDS信頼性の高いトレントを手頃な価格で提供するのが最良の選択肢です。 DASCA Senior Data Scientist 認定 SDS 試験問題 (Q83-Q88):質問 # 83
Maximum Likelihood Estimation (MLE) is a way to frame:
A. Both A and C
B. Small class of problems in Data Science
C. Large class of problems in Data Science
D. Large class of problems in HDFS
E. Small class of problems in HDFS
正解：C
解説：
Maximum Likelihood Estimation (MLE) is a statistical method used to estimate the parameters of a model by maximizing the likelihood function - i.e., finding the parameters that make the observed data most probable.
Option A: Correct. MLE provides a framework for a large class of problems in data science, including regression, classification, generative models, and probabilistic inference.
Option B: Incorrect - it applies to many problems, not just a small subset.
Option C & D: Incorrect. HDFS (Hadoop Distributed File System) is a storage technology, unrelated to MLE.
Option E: Incorrect because C is invalid.
Thus, the correct answer is Option A (Large class of problems in Data Science).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Foundations: Maximum Likelihood Estimation and Inference in Data Science.
質問 # 84
Which classification steps are performed in inductive techniques?
i. Training Step
ii. Test Step
iii. Validation Step
iv. Application Step
A. ii, iii
B. i, ii
C. i, ii, iii, iv
D. i, ii, iv
正解：C
解説：
Inductive learning techniques in machine learning (such as decision trees, neural networks, or SVMs) follow a systematic sequence of steps for classification:
Training Step (i): A model is built using training data, where the system learns relationships between features and target labels.
Test Step (ii): The trained model is evaluated on unseen test data to measure its performance and generalizability.
Validation Step (iii): Often, a validation set is used to fine-tune model parameters, avoid overfitting, and choose the best model configuration.
Application Step (iv): The final validated model is applied to classify new, real-world data.
Since all four steps (i, ii, iii, iv) are essential to inductive classification, the correct answer is Option D (i, ii, iii, iv).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics & Machine Learning: Classification and Inductive Learning Techniques.
質問 # 85
Which of these are open-source column-oriented databases?
A. Both A and B
B. Cassandra
C. Accumulo
D. All of the above
E. HBase
正解：D
解説：
Column-oriented databases store data by columns rather than by rows, enabling efficient queries over large datasets, especially in analytical workloads.
Cassandra (Option A): An open-source, highly scalable, distributed column-oriented NoSQL database.
HBase (Option B): An open-source, Hadoop-based, column-family NoSQL database modeled after Google BigTable.
Accumulo (Option C): An open-source, secure, sorted, distributed key/value store built on top of HDFS and based on Google BigTable.
Since all three (A, B, and C) are open-source column-oriented databases, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Columnar Databases & NoSQL Ecosystem.
質問 # 86
OCR (Optical Character Recognition) is an application used for:
A. Data mining
B. Machine learning
C. MapReduce
D. Big Data Analytics
正解：B
解説：
Optical Character Recognition (OCR) is the process of automatically recognizing and converting different types of documents - such as scanned paper documents, PDFs, or images - into editable and searchable text.
OCR systems use Machine Learning (ML) and Computer Vision techniques to detect and classify patterns of characters in images.
Algorithms like Convolutional Neural Networks (CNNs) are commonly used for image-based OCR.
While OCR may indirectly contribute to data mining or big data workflows, the core application is based on machine learning, where models are trained to classify and recognize text patterns.
Thus, OCR is primarily a Machine Learning application, making Option B correct.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: OCR and Pattern Recognition.
質問 # 87
Which of the following is correct?
A. DataFrame is similar to SQL tables or R data frames
B. Both A and B
C. A data frame is a table with rows and columns
D. All of the above
E. The central object in Pandas is called a DataFrame
正解：D
解説：
Pandas is one of the most widely used Python libraries for data analysis and manipulation. Its central object is the DataFrame.
Option A: Correct. DataFrame is the core data structure in Pandas.
Option B: Correct. DataFrame resembles SQL tables and R data frames, supporting row/column indexing, joins, and grouping.
Option C: Correct. A DataFrame is essentially a 2D labeled table consisting of rows and columns.
Option D: Correct, but not fully inclusive.
Option E: Correct, since all of A, B, and C are true.
Thus, the best answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Pandas Data Structures.