GOOGLE ASSOCIATE-DATA-PRACTITIONER LATEST BRAINDUMPS, ASSOCIATE-DATA-PRACTITIONER EXAM SIMULATOR FEE

Google Associate-Data-Practitioner Latest Braindumps, Associate-Data-Practitioner Exam Simulator Fee

Google Associate-Data-Practitioner Latest Braindumps, Associate-Data-Practitioner Exam Simulator Fee

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Google Associate-Data-Practitioner Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services
Topic 2
  • Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.
Topic 3
  • Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.

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Google Cloud Associate Data Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

  • A. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
  • B. Use Cloud Tasks to schedule and run the jobs asynchronously.
  • C. Create directed acyclic graphs (DAGs) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
  • D. Use Cloud Scheduler to schedule the jobs to run.

Answer: A

Explanation:
UsingCloud Composerto create Directed Acyclic Graphs (DAGs) is the best solution because it is a fully managed, scalable workflow orchestration service based on Apache Airflow. Cloud Composer allows you to define complex task dependencies and schedules while integrating seamlessly with Google Cloud services such as Cloud Storage, BigQuery, and Dataproc for Apache Spark jobs. This approach minimizes operational overhead, supports scheduling and automation, and provides an efficient and fully managed way to orchestrate your data pipelines.
Extract from Google Documentation: From "Cloud Composer Overview" (https://cloud.google.com
/composer/docs):"Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow, enabling you to schedule and automate complex data pipelines with dependencies across Google Cloud services like Cloud Storage, Dataproc, and BigQuery."


NEW QUESTION # 39
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

  • A. Use Colab Enterprise to develop a custom model for purchase prediction.
  • B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
  • C. Use Vertex AI Workbench to develop a custom model for purchase prediction.
  • D. Use BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D


NEW QUESTION # 40
You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) copyright. You need to plan and execute this analysis using the most efficient approach. What should you do?

  • A. Query the BigQuery table from within a Python notebook, use the copyright API to summarize the data within the notebook, and store the summaries in BigQuery.
  • B. Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the copyright API to summarize the data.
  • C. Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use copyright to summarize the data.
  • D. Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the copyright API to summarize the pre- processed data.

Answer: C

Explanation:
Creating a BigQuery Cloud resource connection to a remote model in Vertex AI and using copyright to summarize the data is the most efficient approach. This method allows you to seamlessly integrate BigQuery with the copyright model via Vertex AI, avoiding the need to export data or perform manual steps. It ensures scalability for large datasets and minimizes data movement, leveraging Google Cloud's ecosystem for efficient data summarization and storage.


NEW QUESTION # 41
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

  • A. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
  • B. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
  • C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
  • D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

Answer: C

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:AutoML Natural Language is designed for text classification tasks, including sentiment analysis, and can handle diverse language patterns without extensive preprocessing.
AutoML can train a custom model with minimal coding.
Why other options are incorrect:A: Unnecessary extra preprocessing. AutoML can handle the raw data.
C: Dataproc and Spark are overkill for this task. AutoML is more efficient and easier to use.
D: Developing a custom TensorFlow model requires significant expertise and time, which is not efficient for this scenario.


NEW QUESTION # 42
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

  • A. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
  • B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
  • C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
  • D. Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

Answer: A

Explanation:
Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.


NEW QUESTION # 43
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