QUIZ MARVELOUS GOOGLE - ASSOCIATE-DATA-PRACTITIONER - GOOGLE CLOUD ASSOCIATE DATA PRACTITIONER STUDY REFERENCE

Quiz Marvelous Google - Associate-Data-Practitioner - Google Cloud Associate Data Practitioner Study Reference

Quiz Marvelous Google - Associate-Data-Practitioner - Google Cloud Associate Data Practitioner Study Reference

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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 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.
Topic 3
  • 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.

Google Cloud Associate Data Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

  • A. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
  • B. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
  • C. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
  • D. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Answer: D

Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.


NEW QUESTION # 84
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 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
  • B. 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.
  • C. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
  • D. 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.

Answer: C

Explanation:
Using Dataflow to 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 # 85
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

  • A. Migrate the Spark jobs to Dataproc on Compute Engine.
  • B. Migrate the Spark jobs to Dataproc Serverless.
  • C. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
  • D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

Answer: B

Explanation:
Migrating the Spark jobs toDataproc Serverlessis the best approach because it allows you to run Spark workloads without the need to provision or manage clusters. Dataproc Serverless automatically scales resources based on workload requirements, simplifying operations and reducing administrative overhead. This solution is ideal for organizations that want to focus on managing their Spark code without worrying about the underlying infrastructure. It is cost-effective and fully managed, aligning well with the goal of minimizing cluster management.


NEW QUESTION # 86
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

  • A. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
  • B. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
  • C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
  • D. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Answer: D

Explanation:
To troubleshoot intermittent failures in a Dataflow pipeline, you should use Cloud Logging to view detailed error messages in the pipeline's logs. These logs provide insights into the specific issues causing failures, such as data format errors or resource limitations. Additionally, you should use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization, memory usage, and throughput, to identify performance bottlenecks or resource constraints that may contribute to the failures. This approach provides a comprehensive view of the pipeline's health and helps pinpoint the root cause of the intermittent issues.


NEW QUESTION # 87
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 TB of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

  • A. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
  • B. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
  • C. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
  • D. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

Answer: A

Explanation:
Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.


NEW QUESTION # 88
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