Number of questions: 62 Number of questions to pass: 45 Time allowed: 90 mins Status: Live This intermediate-level certification is intended for developers who are responsible for developing, publishing, configuring, and managing APIs using IBM API Connect 2018.x. This test also covers administration and scripting topics as well but will not cover installation.
This exam consists of four sections described below. For more detail, please see the study guide on the Exam Preparation tab.
Architectural Overview of IBM API Connect 20% Articulate the architectural requirements to support a given IBM API Connect topology Compare the different deployment options Differentiate between the spaces and two types of organizations Demonstrate the various stages in the lifecycle of an API, including Create, Run, Manage, Secure, Test, and Monitor Distinguish between the various roles involved in the lifecycle of an API Implement the OpenAPI specification +Identify typical use cases across industry
Cloud/API Manager Role 21% Configure and manage the IBM API Connect cloud components +Manage the IBM API Connect Cloud using the REST interface Use the IBM API Connect Command Line Interface Backup and restore IBM API Connect configuration data Backup and restore APIs and Products Analyze logs to identify problems within the IBM API Connect Cloud Secure the IBM API Connect Cloud Integrate with an external user registry Configure the API Gateway extensions Manage IBM API Connect catalogs
API Developer Role 27% Create and configure a SOAP API Create and configure a REST API Apply a security definition to an API Leverage API assembly components Use the Unit Testing tools to test APIs Implement user-defined policies Manage error handling Utilize API properties Use the IBM API Connect Developer Toolkit Command Line Interface
Product Manager Role 16% Distinguish between the various lifecycle stages of APIs and Products Gain business insight from analytics information Show the relationship between Products and Plans and APIs Design Products and Plans Administer Consumer Access
Developer Portal 16% Distinguish between the various lifecycle stages of APIs and Products Gain business insight from analytics information Show the relationship between Products and Plans and APIs Design Products and Plans Administer Consumer Access
The sample test is designed to give the candidate an idea of the content and format of the questions that will be on the certification exam. Performance on the sample test is NOT an indicator of performance on the certification exam. This should not be considered an assessment tool.
Use the study guide to help pass this exam. A study guide is an easy to follow document that will help you prepare for this exam. The guide is free and can be downloaded immediately.
This exam has an Assessment Exam option: A1000-044 Assessment: IBM API Connect 2018.x Solution Implementation
Assessment exams are web-based exams that provides you, at a cheaper costs, the ability to check your skills before taking the certification exam.
This assessment exam is available in: English
Passing the exam does not award you a certification, and it is only used to help you assess if you are ready or not to take the certification exam.
You can register for it at Pearson VUE and it will provide you a score report, showing you how you did in each section.
All IBM certification tests presume a certain amount of “on-the-job” experience which is not present in any classroom or Web presentation. The recommended courses and links will help you gain the skill and product knowledge represented in the test objectives. They do not teach the answers to the test questions and are not intended to do so. This information may not cover all subject areas in the certification test or may contain more recent information than is present in the certification test. Taking these or any classes will not guarantee that you will achieve certification.
Learning Path Solution Developer: IBM API Connect Build skills to to help you create developer communities to publish and share APIs and engage with them through a self-service portal.
Professional Data Engineer A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.
The Professional Data Engineer exam assesses your ability to: Design data processing systems Build and operationalize data processing systems Operationalize machine learning models Ensure solution quality
About this certification exam Length: 2 hours Registration fee: $200 (plus tax where applicable) Languages: English, Japanese. Exam format: Multiple choice and multiple select, taken in person at a test center. Locate a test center near you. Prerequisites: None Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using GCP.
Hands-on practice This exam is designed to test technical skills related to the job role. Hands-on experience is the best preparation for the exam. If you feel you may need more experience or practice, use the hands-on labs available on Qwiklabs as well as the GCP free tier to level up your knowledge and skills.
4. Practice exam Check your readiness to take the exam. Not feeling quite ready? Check out the additional resources listed below and get more hands-on practice with Qwiklabs.
5. Additional resources In-depth discussions on the concepts and critical components of GCP: Google Cloud documentation Google Cloud solutions
6. Schedule your exam Register and find a location near you.
1. Designing data processing systems 1.1 Selecting the appropriate storage technologies. Considerations include: Mapping storage systems to business requirements Data modeling Tradeoffs involving latency, throughput, transactions Distributed systems Schema design
1.2 Designing data pipelines. Considerations include: Data publishing and visualization (e.g., BigQuery) Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka) Online (interactive) vs. batch predictions Job automation and orchestration (e.g., Cloud Composer)
1.3 Designing a data processing solution. Considerations include: Choice of infrastructure System availability and fault tolerance Use of distributed systems Capacity planning Hybrid cloud and edge computing Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions) At least once, in-order, and exactly once, etc., event processing
1.4 Migrating data warehousing and data processing. Considerations include: Awareness of current state and how to migrate a design to a future state Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking) Validating a migration
2. Building and operationalizing data processing systems
2.1 Building and operationalizing storage systems. Considerations include: Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore) Storage costs and performance Lifecycle management of data
2.2 Building and operationalizing pipelines. Considerations include: Data cleansing Batch and streaming Transformation Data acquisition and import Integrating with new data sources
2.3 Building and operationalizing processing infrastructure. Considerations include: Provisioning resources Monitoring pipelines Adjusting pipelines Testing and quality control
3. Operationalizing machine learning models
3.1 Leveraging pre-built ML models as a service. Considerations include: ML APIs (e.g., Vision API, Speech API) Customizing ML APIs (e.g., AutoML Vision, Auto ML text) Conversational experiences (e.g., Dialogflow)
3.2 Deploying an ML pipeline. Considerations include: Ingesting appropriate data Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML) Continuous evaluation
3.3 Choosing the appropriate training and serving infrastructure. Considerations include: Distributed vs. single machine Use of edge compute Hardware accelerators (e.g., GPU, TPU)
3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include: Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics) Impact of dependencies of machine learning models Common sources of error (e.g., assumptions about data)
4. Ensuring solution quality
4.1 Designing for security and compliance. Considerations include: Identity and access management (e.g., Cloud IAM) Data security (encryption, key management) Ensuring privacy (e.g., Data Loss Prevention API) Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
4.2 Ensuring scalability and efficiency. Considerations include: Building and running test suites Pipeline monitoring (e.g., Stackdriver) Assessing, troubleshooting, and improving data representations and data processing infrastructure Resizing and autoscaling resources
4.3 Ensuring reliability and fidelity. Considerations include: Performing data preparation and quality control (e.g., Cloud Dataprep) Verification and monitoring Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis) Choosing between ACID, idempotent, eventually consistent requirements
4.4 Ensuring flexibility and portability. Considerations include: Mapping to current and future business requirements Designing for data and application portability (e.g., multi-cloud, data residency requirements) Data staging, cataloging, and discovery
QUESTION 1 Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
A. Threading B. Serialization C. Dropout Methods D. Dimensionality Reduction
Correct Answer: C
QUESTION 2 You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?
A. Continuously retrain the model on just the new data. B. Continuously retrain the model on a combination of existing data and the new data. C. Train on the existing data while using the new data as your test set. D. Train on the new data while using the existing data as your test set.
Correct Answer: B
QUESTION 3 You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
A. Add capacity (memory and disk space) to the database server by the order of 200. B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges. C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join. D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
Correct Answer: C
QUESTION 4 You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?
A. Disable caching by editing the report settings. B. Disable caching in BigQuery by editing table details. C. Refresh your browser tab showing the visualizations. D. Clear your browser history for the past hour then reload the tab showing the virtualizations.
Correct Answer: A
QUESTION 5 An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?
A. Use federated data sources, and check data in the SQL query. B. Enable BigQuery monitoring in Google Stackdriver and create an alert. C. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0. D. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.
QUESTION 1 Which two statements describe the role of the cache manifest in a mobile app? (Choose two.)
A. Allows downloading of rules for offline use with a mobile app. B. Provides access to static resources such as HTML files, image files, or JS files. C. Enables users to continue interacting with mobile apps while offline. D. Supports debugging efforts by providing a run-time view of the rules accessed by the app.
Correct Answer: BC
QUESTION 2 Which two design configurations limit the need for horizontal scrolling when an application is used on a mobile device? (Choose two.)
A. Use grid layouts rather than repeating dynamic layouts to display tabular data. B. Set the importance for columns in repeating dynamic layouts. C. Limit text fields to a width of 200 pixels. D. Set the width for layouts in percentages.
Correct Answer: BD
QUESTION 3 You want to allow users to use an application on a mobile device, even if the device is not connected to a network. Which configuration option supports this requirement?
A. Simulate external data sources when the application is offline. B. Source repeating layouts using report definitions. C. Configure UI elements to use native controls on mobile devices. D. Source drop-down lists using data pages.
Correct Answer: D
QUESTION 4 Offline support requires which two configurations? (Choose two.)
A. Access groups set up to allow offline access to users. B. Appropriate case types configured for offline processing. C. An authorization activity to manage offline permissions. D. A set of privileges to run sections in an offline environment.
