Die IBM watsonx Generative AI Engineer - Associate Prüfung gilt als anspruchsvoll, und viele Kandidaten unterschätzen das Niveau der C1000-185 Fragen. Die 380 Praxisfragen von ExamFragen zeigen Ihnen realistisch, was Sie im Prüfungsraum erwartet.

IBM C1000-185 Prüfungsübersicht:

Zertifizierungsanbieter:	IBM
Prüfungsname:	IBM watsonx Generative AI Engineer v1 - Associate
Prüfungsnummer:	C1000-185
Prüfungsgebühr:	200 USD
Prüfungsdauer:	90 Minuten
Verfügbare Sprachen:	Englisch
Gültigkeitsdauer des Zertifikats:	3 Jahre
Anzahl der Fragen:	62
Mindestpunktzahl:	44/62 (ca. 71 %)
Prüfungsformat:	Mehrfachauswahl, Einzelauswahl
Empfohlenes Training:	Lernpfad zur Zertifizierung IBM Certified watsonx Generative AI Engineer v1.1 - Associate
Prüfungsanmeldung:	Registrierung für IBM-Zertifizierungen und bei Pearson VUE
Beispielfragen:
Prüfungsmethode:	Online-Prüfung mit Aufsicht oder Vor-Ort-Prüfung in einem Pearson VUE-Prüfungszentrum
Voraussetzungen:	Grundlegendes Verständnis von KI-/ML-Konzepten; Kenntnisse in der Programmierung mit Python werden empfohlen; es gibt keine verbindlichen Teilnahmevoraussetzungen
Offizielle Syllabus-URL:	https://www.ibm.com/training/certification/ibm-certified-watsonx-generative-ai-engineer-associate-C9007000

IBM C1000-185 Prüfungsthemen:

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IBM watsonx Generative AI Engineer - Associate C1000-185 Prüfungsfragen mit Lösungen

Frage #1

A company is considering using IBM Watsonx for two different use cases: (1) automating email responses for routine inquiries from customers, and (2) generating creative marketing copy for new product campaigns.
Which of the following best describes the model selection process for these use cases?

A. Choose a domain-specific model for automating email responses and a more generalized model with creative capabilities for generating marketing copy.
B. Use the smallest possible model to reduce computational costs, regardless of the use case.
C. A single model should be used for both use cases since Watsonx can handle any type of text generation task equally well.
D. Use a large language model fine-tuned for email response generation for both tasks, as the fine-tuning process will enable the model to handle creative tasks as well.

Frage #2

You are tasked with inspecting and validating a dataset using IBM Watson's Data Refinery tool. The dataset will be used for an AI application that performs natural language processing (NLP) on customer support tickets. Upon inspection, you discover that the 'ticket_description' field contains numerous instances of null values and inconsistent formats.
What is the most appropriate action to take next using Data Refinery?

A. Use Data Refinery's "Data Quality" feature to generate a profiling report and assess the scope of the issues.
B. Replace all null values with zeros, as this will standardize the dataset.
C. Proceed with training the model without any changes, as missing data will not impact NLP models significantly.
D. Delete all rows containing null values in the 'ticket_description' field.

Frage #3

In a RAG system, the retriever is responsible for fetching relevant documents or information from a knowledge base based on the input query. Different retriever types can be used depending on the nature of the task.
Which retriever type is most suitable for a RAG system that requires efficient large-scale retrieval from a document corpus based on semantic similarity?

A. Lexical Retriever: Primarily returns results based on syntactic similarity, relying on word order and surface-level features of the input query.
B. Hybrid Retriever: Combines syntactic retrieval methods (like BM25) with semantic retrieval (like dense retrieval) but sacrifices retrieval speed for accuracy.
C. Dense Retriever: Uses vector embeddings to retrieve documents based on the semantic similarity of the input query and stored documents.
D. Exact-Match Retriever: Returns documents based solely on keyword matching and is optimized for highly structured, labeled datasets.

Frage #4

You are designing a question-answering system that can provide responses based on a vast corpus of legal documents. Your solution leverages the Retrieval-Augmented Generation (RAG) pattern using an IBM watsonx-based architecture. The goal is to ensure that the system retrieves highly relevant documents and uses the retrieved content to generate accurate responses. The team is considering various libraries to integrate dense retrieval and generation models into this architecture.
Which of the following libraries or frameworks should you integrate to effectively implement the RAG pattern in this scenario, considering the need for both document retrieval and natural language generation?

A. PyTorch for implementing rule-based retrieval and generative model fine-tuning.
B. TensorFlow for dense retrieval and document embedding generation.
C. Hugging Face Transformers for language generation and FAISS for embedding-based document retrieval.
D. Keras for handling sparse retrieval and pre-trained generation models.

Frage #5

You are developing a Retrieval-Augmented Generation (RAG) system using IBM WatsonX LLM and a vector database. Your dataset consists of long legal documents, and you want to ensure the system retrieves the most relevant sections of these documents efficiently.
Which of the following best describes the appropriate approach to text chunking for this RAG implementation?

A. Chunking the documents at arbitrary points, ignoring sentence or paragraph boundaries to enhance retrieval speed.
B. Splitting the legal documents into fixed-size chunks of 10,000 tokens each to maximize retrieval accuracy.
C. Chunking the documents based solely on page numbers, as legal documents typically follow consistent formatting.
D. Splitting the documents into smaller chunks based on logical or semantic breaks such as paragraphs, while maintaining a token count that matches the LLM's context window.

Fragen und Antworten: