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New Test Associate-Data-Practitioner Dumps Demo | Pass-Sure Associate-Data-Practitioner: Google Cloud Associate Data Practitioner 100% Pass

New Test Associate-Data-Practitioner Dumps Demo | Pass-Sure Associate-Data-Practitioner: Google Cloud Associate Data Practitioner 100% Pass

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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 (Q93-Q98):

NEW QUESTION # 93
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement,and previous click behavior. After training the model, you want to generate predictions on new data. Which model type should you use in BigQuery ML?

  • A. Logistic regression
  • B. Linear regression
  • C. K-means clustering
  • D. Matrix factorization

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model.
BigQuery ML supports this use case with logistic regression.
* Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.
* Option B: Matrix factorization is for recommendation systems, not binary prediction.
* Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.


NEW QUESTION # 94
Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

  • A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
  • B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
  • C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
  • D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Answer: D

Explanation:
Creating asingle Explorewith all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker's recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
* Option A: Materialized views in BigQuery optimize queries but bypass Looker's modeling layer, reducing flexibility.
* Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering
/drill-down.
* Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.


NEW QUESTION # 95
You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process.
Your solution should minimize costs where possible. What should you do?

  • A. Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the - custom-storage-class flag.
  • B. Create parallel transfer jobs using include and exclude prefixes.
  • C. Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
  • D. Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.
This can significantly increase throughput and reduce the overall transfer time.
Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.
C: Dataflow is a complex solution for a simple file transfer task.
D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.


NEW QUESTION # 96
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. Use Cloud Tasks to schedule and run the jobs asynchronously.
  • B. Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
  • 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: B

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 # 97
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

  • A. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
  • B. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
  • C. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
  • D. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

Answer: D

Explanation:
UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time.
It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.


NEW QUESTION # 98
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