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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Pipeline automation and scheduling 1. Error handling and monitoring 2. Schedule and monitor data jobs - Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
Data Analysis and Presentation	27%	- Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Data exploration and analysis 1. BigQuery SQL queries 2. Jupyter notebooks for analysis 3. Identify trends, patterns, insights - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data
Data Preparation and Ingestion	30%	- Storage solutions selection 1. Storage location types: regional, dual-regional, multi-regional, zonal 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner - Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries
Data Management and Governance	25%	- Data security and access control 1. IAM roles and permissions 2. Data encryption and protection - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - Compliance and governance 1. Data stewardship and cataloging 2. Data privacy and regulatory requirements

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ADP Übungstest: Associate Data Practitioner & ADP Braindumps Prüfung

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Google Associate Data Practitioner ADP Prüfungsfragen mit Lösungen (Q49-Q54):

49. Frage
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Colab Enterprise to develop a custom model for purchase prediction.
B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
C. Use BigQuery ML to create a logistic regression model for purchase prediction.
D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Antwort: C

50. Frage
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
B. Apply a filter to only show products with a positive profit margin.
C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Antwort: A

51. Frage
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
B. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
C. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
D. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.

Antwort: B

52. Frage
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 the pipeline as a set of PTransforms in Dataflow. Restart the pipeline 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 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.

Antwort: A

53. Frage
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A. Use Cloud Composer to orchestrate and run data workflows.
B. Use Dataform to build, orchestrate, and monitor the pipelines.
C. Use Cloud Data Fusion to visually design and manage the pipelines.
D. Use Dataproc to run MapReduce jobs for distributed data processing.

Antwort: B

54. Frage
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