ADP測試引擎 & ADP認證

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing and Analytics	20-30%	- Query and analyze datasets - Use BigQuery and SQL for analytics - Aggregate and summarize data - Build and maintain data pipelines - Apply statistical methods for analysis
Topic 2: Data-Driven Decision Making	10-20%	- Define success metrics - Translate business requirements into data solutions - Identify stakeholders and requirements - Assess data quality and completeness
Topic 3: Data Visualization and Insights	20-30%	- Build visualizations using Looker Studio - Create dashboards and reports - Interpret and communicate findings - Choose appropriate visualization types - Present data insights to stakeholders
Topic 4: Data Preparation and Exploration	20-30%	- Transform and prepare data for analysis - Ingest and acquire data - Perform exploratory data analysis (EDA) - Identify data quality issues - Explore data through visualization and queries

>> ADP測試引擎 <<

ADP認證 & ADP信息資訊

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最新的 Google Cloud Certified ADP 免費考試真題 (Q40-Q45):

問題 #40
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

答案：A

問題 #41
Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
B. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
C. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
D. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

答案：C

問題 #42
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
B. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
C. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
D. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

答案：A

問題 #43
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set temporary object holds.
B. Set a bucket retention policy.
C. Enable Object Versioning.
D. Change the bucket storage class to Archive.

答案：B

問題 #44
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 processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task 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 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.
D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

答案：A

問題 #45
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