ADP Valid Exam Papers - Test ADP Questions Pdf

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
C. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Answer: B

NEW QUESTION # 37
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 Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D. 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.

Answer: D

NEW QUESTION # 38
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. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
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. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Answer: C

NEW QUESTION # 39
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 a bucket retention policy.
B. Change the bucket storage class to Archive.
C. Enable Object Versioning.
D. Set temporary object holds.

Answer: A

NEW QUESTION # 40
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Cloud Data Fusion to visually design and manage the pipeline.
B. Use Dataproc to run the transformations on a cluster.
C. Use Dataflow with a custom template for the transformation logic.
D. Use Dataform to define the transformations in SQLX.

Answer: B

NEW QUESTION # 41
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