Tech4Examはどんな学習資料を提供していますか？

現代技術は人々の生活と働きの仕方を革新します（DSA-C03試験学習資料）。 広く普及しているオンラインシステムとプラットフォームは最近の現象となり、IT業界は最も見通しがある業界（DSA-C03試験認定）となっています。 企業や機関では、候補者に優れた教育の背景が必要であるという事実にもかかわらず、プロフェッショナル認定のようなその他の要件があります。それを考慮すると、適切なSnowflake　SnowPro Advanced: Data Scientist Certification Exam試験認定は候補者が高給と昇進を得られるのを助けます。

SnowPro Advanced: Data Scientist Certification Exam試験学習資料での高い復習効率

ほとんどの候補者にとって、特にオフィスワーカー、DSA-C03試験の準備は、多くの時間とエネルギーを必要とする難しい作業です。だから、適切なDSA-C03試験資料を選択することは、DSA-C03試験にうまく合格するのに重要です。高い正確率があるDSA-C03有効学習資料によって、候補者はSnowPro Advanced: Data Scientist Certification Exam試験のキーポイントを捉え、試験の内容を熟知します。あなたは約2日の時間をかけて我々のDSA-C03試験学習資料を練習し、DSA-C03試験に簡単でパスします。

無料デモをごダウンロードいただけます

様々な復習資料が市場に出ていることから、多くの候補者は、どの資料が適切かを知りません。この状況を考慮に入れて、私たちはSnowflake　DSA-C03の無料ダウンロードデモを候補者に提供します。弊社のウェブサイトにアクセスしてSnowPro Advanced: Data Scientist Certification Examデモをダウンロードするだけで、DSA-C03試験復習問題を購入するかどうかを判断するのに役立ちます。多数の新旧の顧客の訪問が当社の能力を証明しています。私たちのDSA-C03試験の学習教材は、私たちの市場におけるファーストクラスのものであり、あなたにとっても良い選択だと確信しています。

DSA-C03試験認定を取られるメリット

ほとんどの企業では従業員が専門試験の認定資格を取得する必要があるため、DSA-C03試験の認定資格がどれほど重要であるかわかります。テストに合格すれば、昇進のチャンスとより高い給料を得ることができます。あなたのプロフェッショナルな能力が権威によって認められると、それはあなたが急速に発展している情報技術に優れていることを意味し、上司や大学から注目を受けます。より明るい未来とより良い生活のために私たちの信頼性の高いDSA-C03最新試験問題集を選択しましょう。

DSA-C03試験学習資料を開発する専業チーム

私たちはDSA-C03試験認定分野でよく知られる会社として、プロのチームにSnowPro Advanced: Data Scientist Certification Exam試験復習問題の研究と開発に専念する多くの専門家があります。したがって、我々のSnowPro Advanced試験学習資料がDSA-C03試験の一流復習資料であることを保証することができます。私たちは、SnowPro Advanced　DSA-C03試験サンプル問題の研究に約10年間集中して、候補者がDSA-C03試験に合格するという目標を決して変更しません。私たちのDSA-C03試験学習資料の質は、Snowflake専門家の努力によって保証されています。それで、あなたは弊社を信じて、我々のSnowPro Advanced: Data Scientist Certification Exam最新テスト問題集を選んでいます。

デモをダウンロードする

Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題:

1. You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

A) Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).
B) Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
C) Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
D) Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).
E) Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.

2. A healthcare provider has a Snowflake table 'MEDICAL RECORDS containing patient notes stored as unstructured text in a column called 'NOTE TEXT. They want to identify different patient groups based on the topics discussed in these notes. They aim to use a combination of unsupervised and supervised learning. Which of the following represents a robust workflow to achieve this goal?

A) Export all 'NOTE TEXT data to an extemal system, use an existing NLP pipeline for topic modeling and manual labeling, then create a Snowflake UDF that replicates this entire pipeline internally.
B) Perform topic modeling on a sample of the 'NOTE TEXT data using a Snowflake Python UDF. Manually review the top documents for each identified topic, and assign labels describing the patient group represented by each topic. Train a supervised multi-label classification model (e.g., using scikit-learn's
C) MultiOutputClassifier wrapped around a Logistic Regression model) within Snowflake (using Snowpark), using the original 'NOTE TEXT as input features (TF-IDF or word embeddings) and the manually assigned topic labels as target variables. Use the trained model to classify the remaining patient notes into relevant patient groups.
D) Use a Snowflake external function to call a pre-trained topic modeling model (e.g., BERTopic) hosted on Google Cloud A1 Platform. Assign topic probabilities to each patient note. Then, perform K-Means clustering on the topic probabilities to identify patient segments. No manual labeling is performed.
E) Perform topic modeling (e.g., LDA) directly on the 'NOTE_TEXT column using a Python UDF in Snowflake. Manually label a subset of the resulting topics. Then, train a supervised classifier (e.g., Naive Bayes) to predict the identified topics for new patient notes.

3. A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

A)

B)

C)

D)

E)

4. You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A) Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
B) Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
C) Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.
D) Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
E) Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.

5. A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

A) Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
B) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
C) Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
D) Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.
E) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.

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