Valid Braindumps ADP Sheet & ADP Sample Questions Pdf

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Google ADP Exam Syllabus Topics:

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

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

NEW QUESTION # 36
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
B. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
C. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
D. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

Answer: A

NEW QUESTION # 37
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Store all data in a single BigQuery table without partitioning or lifecycle policies.
B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
D. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Answer: D

NEW QUESTION # 38
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. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

NEW QUESTION # 39
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: C

NEW QUESTION # 40
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
B. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Answer: C

NEW QUESTION # 41
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